Compressing image-based data using luminance

Information

  • Patent Grant
  • 8594441
  • Patent Number
    8,594,441
  • Date Filed
    Tuesday, September 12, 2006
    17 years ago
  • Date Issued
    Tuesday, November 26, 2013
    10 years ago
Abstract
Image-based data, such as a block of texel data, is accessed. The data includes sets of color component values. A luminance value is computed for each set of color components values, generating a range of luminance values. A first set and a second set of color component values that correspond to the minimum and maximum luminance values are selected from the sets of color component values. A third set of color component values can be mapped to an index that identifies how the color component values of the third set can be decoded using the color component values of the first and second sets. The index value is selected by determining where the luminance value for the third set lies in the range of luminance values.
Description
RELATED UNITED STATES PATENT APPLICATIONS

This Application is related to U.S. patent application Ser. No. 11/520,066 by G. King et al., filed Sep. 12, 2006, and entitled “Decompressing Image-Based Data Compressed Using Luminance,” assigned to the assignee of the present invention, and hereby incorporated by reference in its entirety.


FIELD OF THE INVENTION

Embodiments of the present invention generally relate to data processing. More specifically, embodiments of the present invention relate to data compression.


BACKGROUND ART

Texture data can be compressed (encoded) to reduce the amount of memory required to store textures and to reduce the bandwidth needed to read the textures. One commonly used texture compression scheme is S3 Texture Compression (S3TC), also referred to as DirectX Texture Compression (DXTC or DXTn). “True-color” images use eight (8) bits for each set of red (R), green (G) and blue (B) components in a texel—24 bits per texel and 384 bits for a four-by-four (4×4) block of 16 texels. With S3TC, a block of texel data can be reduced to 64 bits, a compression ratio of six-to-one.


This is accomplished in S3TC by first using the 16 “RGB-tuples” (a set of RGB component values that describe a color) in a 4×4 block of texels to calculate two (2) representative colors C0 and C1, which are stored as 16 bits each (32 bits total) in RGB565 format. More specifically, the representative colors C0 and C1 are computed as the eigenvalues of the inertia tensor matrix defined by the 16 texels in a 4×4 block.


Each of the 16 RGB-tuples is then mapped to a line in RGB space that has the representative colors C0 and C1 as endpoints. Each RGB-tuple is associated with an index value depending on where the set of colors falls on that line. More specifically, the indices are computed by finding the perpendicular intersection between the line in RGB space and a line to each texel's colors. The 16 indices can each be represented using 2 bits—an additional 32 bits and a total of 64 bits per 4×4 block.


Although S3TC effectively compresses the data while maintaining the quality of the decoded (decompressed) image, the selection of the representative colors C0 and C1 and the computation of the indices can require relatively sophisticated processing.


SUMMARY OF THE INVENTION

Accordingly, a less complex compression scheme that still provides acceptable image quality would be advantageous. Embodiments in accordance with the present invention provide these and other advantages.


In one embodiment, image-based data, such as a block of texel data, is accessed. The data includes a plurality of sets of color component values (the sets may also be referred to as RGB-tuples, or “tuples” in general; each set of color component values describes a color). A luminance value is computed for each set of color component values, generating a range of luminance values. A first set and a second set of color component values, describing a first color and a second color, are selected from the sets of color component values. The first and second colors correspond to the minimum and maximum luminance values in the range of luminance values. A third set of color component values, describing a third color, can be mapped to an index. Generally speaking, the index identifies how the third color can be decoded (e.g., interpolated) using the first and second colors. The index value is selected by determining where the luminance value for the third color lies in the range of luminance values.


In one such embodiment, for each texel in a block of texels, each color component value is reduced in length. For example, for true-color (8 bit) values, each color component value is reduced to six (6) bits.


For each texel in the block of texels, a luminance value can be determined using the respective color component values (e.g., the R, G and B values) associated with the texel. In one embodiment, the luminance is computed as the dot product 0.299R+0.587G+0.1146. In another embodiment, the dot product is computed in hardware using integer shifters and adders, as follows: the red value is left-shifted by one bit, the green value is left-shifted by two bits, and the luminance is determined by adding the resultant red and green values and the blue value.


In one embodiment, for each block of texels, the minimum and maximum luminance values are determined, and the tuples associated with the minimum and maximum luminance values are selected as the representative colors C0 and C1. The minimum and maximum luminance values are the endpoints of the range of luminance values. In one embodiment, the luminance value computed for each for each texel in the block is compared to the range of luminance values. In one such embodiment, an index value is associated with each tuple (and hence each texel) depending on where the luminance value computed using that tuple lies in the range of luminance values. The index value identifies how each tuple is to be interpolated using C0 and C1.


During decoding, the representative colors C0 and C1 and the index values are used to reconstruct the encoded sets of color component values, each set defining a color. More specifically, during decoding, sets of color component values are interpolated using the representative first and second sets of color component values, and the index values identify how the interpolation is to be performed.


Reconstructed images produced from raw (natural) image data that is compressed as described above are perceptually comparable in quality to reconstructed images produced according to S3TC. However, compression performed in accordance with embodiments of the present invention requires less sophisticated processing relative to S3TC. Furthermore, embodiments in accordance with the present invention are less costly in terms of both area and power consumption. Also, embodiments in accordance with the present invention can be readily implemented in hardware as well as software. Moreover, embodiments in accordance with the present invention are parallelizable and are compatible with existing S3TC engines. In addition, embodiments in accordance with the present invention allow applications to store runtime-generated images in a compressed format, reducing the amount of memory and bandwidth required for dynamic data, which can be of particular value on handheld platforms such as digital cameras.


These and other objects and advantages of the various embodiments of the present invention will be recognized by those of ordinary skill in the art after reading the following detailed description of the embodiments that are illustrated in the various drawing figures.





BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the present invention and, together with the description, serve to explain the principles of the invention.



FIG. 1 illustrates a block of texels mapped to a range of luminance values according to an embodiment of the present invention.



FIG. 2 illustrates a mapping between a range of luminance values and a range of colors according to an embodiment of the present invention.



FIG. 3 is a flowchart of a method for processing (e.g., compressing) data according to an embodiment of the present invention.



FIG. 4 is a flowchart of a method for processing (e.g., decompressing) data according to an embodiment of the present invention.



FIG. 5 is a block diagram of one embodiment of a system for compressing data in accordance with the present invention.





DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail to the various embodiments of the present invention, examples of which are illustrated in the accompanying drawings. While the invention will be described in conjunction with these embodiments, it will be understood that they are not intended to limit the invention to these embodiments. On the contrary, the invention is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be understood that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present invention.


Some portions of the detailed descriptions that follow are presented in terms of procedures, logic blocks, processing, and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those utilizing physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as transactions, bits, values, elements, symbols, characters, samples, pixels, or the like.


It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present invention, discussions utilizing terms such as “accessing,” “selecting,” “encoding,” “defining,” “using,” “determining,” “computing,” “shifting,” “adding,” “identifying,” “reducing,” “truncating,” “associating,” “comparing” or the like, refer to actions and processes (e.g., flowcharts 300 and 400 of FIGS. 3 and 4, respectively) of a computer system or similar electronic computing device or processor (e.g., system 500 of FIG. 5). The computer system or similar electronic computing device manipulates and transforms data represented as physical (electronic) quantities within the computer system memories, registers or other such information storage, transmission or display devices.


The descriptions and examples provided herein are discussed in the context of image-based data in general and texel data in particular; however, the present invention is not so limited. In general, the present invention, in its various embodiments, is also well-suited for use with pixel data, video data, still-image data, Web page-based data, graphics data and the like, and combinations thereof. In addition, the data may be multimedia data; for example, there may be audio data associated with the video data.


Embodiments in accordance with the present invention are applicable to, but not limited to, DXT1, DXT2, DXT3, DXT4 and DXT5. DXT1 does not include an alpha channel, and color data for a 4×4 texel is encoded in 64 bits. DXT2-5 include an alpha channel, and a 4×4 texel is encoded in 128 bits-64 bits are used for the alpha channel data and 64 bits are used for color data. The discussion below pertains to the encoding of the color data of DXT1-5.



FIG. 1 illustrates a block 10 of texture elements (texels) mapped to a range 15 of luminance values according to an embodiment of the present invention. Block 10 represents a portion of, for example, a frame of raw or natural (e.g., uncompressed) image data. In the example of FIG. 1, block 10 includes 16 texels arranged as a 4×4 array; however, the present invention is not so limited.


Texel 11 represents an example texel i. Associated with each texel i is a set of color component values (e.g., a tuple or RGB-tuple consisting of a red value Ri, a green value Gi, and a blue value Bi) that describe a color for that texel. In one embodiment, the color component values Ri, Gi and Bi are each 8 bits in length.


According to embodiments of the present invention, for a texel i, the color component values Ri, Gi and Bi are used to compute a luminance value Li for that texel. In one embodiment, before the luminance value is computed, the color component values Ri, Gi and Bi are reduced in length from 8 bits each to 6 bits each. In one such embodiment, this is accomplished by truncating the least significant bits from the color component values.


In S3TC, encoded color component values are stored in the RGB565 format (five [5] bits for the red and blue values, and 6 bits for the green value). Hence, by reducing the color component values from 8 bits to 6 bits, a level of precision that is at least equivalent to the minimum level of precision associated with S3TC is maintained.


In one embodiment, the luminance value L; is computed as the dot product 0.299Ri+0.587Gi+0.114Bi (equation 1). In another embodiment, the dot product is computed as follows: the red value Ri is left-shifted by one (1) bit, the green value Gi is left-shifted by 2 bits, and the luminance Li is determined by adding the resultant red and green values and the blue value Bi. In the latter embodiment, the coefficients of equation 1 are approximated as 2/7, 4/7 and 1/7, respectively. An advantage of the latter embodiment is that it can be computed using integer shifters and adders and is thus readily implemented in hardware.


Luminance values can be determined from color component values in other ways. For example, the red and blue values can be disregarded, and the green value can be used as the luminance value.


Thus, a range 15 of luminance values is determined for each texel in the block 10 of texels. In the present embodiment, the minimum luminance value Lmin and the maximum luminance value Lmax in the range 15 are then identified. In the example of FIG. 1, the minimum luminance value is determined using the set of color component values (the RGB-tuple) associated with texel 12, and the maximum luminance value is determined using the set of color component values associated with texel 13. Accordingly, in the example of FIG. 1, the set of color component values associated with texel 12 is selected as representative color C0, and the set of color component values associated with texel 13 is selected as representative color C1. In one embodiment, the colors C0 and C1 are each truncated to RGB565 format.


Significantly, according to embodiments of the present invention, the representative colors C0 and C1 are not calculated as in S3TC. Instead, according to embodiments of the present invention, the representative colors C0 and C1 are selected from the existent set of colors associated with block 10 of texel data. That is, in the example of FIG. 1, there are 16 RGB-tuples (16 sets of color component values, each set describing a color) associated with block 10, and two of those sets (colors) are selected as C0 and C1. Because C0 and C1 are selected from, instead of calculated from, the existent RGB-tuples or colors associated with block 10, computational complexity is reduced.



FIG. 2 illustrates a mapping between a range 15 of luminance values and a range 25 of colors according to an embodiment of the present invention. In the example of FIG. 2, the lines representing the ranges 15 and 25 are drawn in parallel and are of equal length; however, this may not actually be the case.


In the present embodiment, the luminance values Li are each compared to the range 15 of luminance values to determine where they lie in relation to the range 15. In one embodiment, the range 15 is essentially divided into 4 sub-ranges I, II, III and IV. Each sub-range is demarcated by an upper limit and a lower limit; in other words, each sub-range is delimited by a respective upper threshold value. In the example of FIG. 2, sub-range I is delimited by an upper threshold value T1 that is ⅙ the distance between Lmin and Lmax; sub-range II is delimited by an upper threshold value T2 that is 3/6 the distance between Lmin and Lmax; sub-range III is delimited by an upper threshold value T3 that is ⅚ the distance between Lmin and Lmax; and sub-range IV is delimited by an upper threshold value of Lmax. In terms of the minimum and maximum luminance values, T1=(5Lmin+Lmax)/6; T2=(3Lmin+3Lmax)/6; and T3=(Lmin+5Lmax)/6.


To determine which of the sub-ranges the luminance values Li lie within, each luminance value Li associated with block 10 (FIG. 1) can be compared to the thresholds T1, T2 and T3 mentioned above. In one embodiment, a three-bit comparison vector 22 is generated based on the results of the comparisons. In such an embodiment, if Li is less than T1, then Li is in sub-range I and the comparison vector 22 has a value of (000); if Li is greater than or equal to T1 but less than T2, then Li is in sub-range II and the comparison vector 22 has a value of (100); if Li is greater than or equal to T2 but less than T3, then Li is in sub-range III and the comparison vector 22 has a value of (110); and if Li is greater than or equal to T3, then Li is in sub-range IV and the comparison vector 22 has a value of (111).


Thus, a comparison vector 22 is associated with each luminance value Li. Because each luminance value Li is associated with (computed from) a set of color component values Ri, Gi and Bi, by extension a comparison vector 22 is associated with each set of color component values in block 10 (FIG. 1).


Continuing with reference to FIG. 2, each comparison vector 22 is mapped to an index value 28. It may instead be said that each sub-range I, II, III and IV is mapped to an index value 28, or that each comparison vector 22 or each sub-range is mapped to a point on range 25 of colors, and in turn points on range 25 are mapped to index values 28.


In any case, luminance values in sub-range I are mapped to C0, luminance values in sub-range II are mapped to a point in color range 25 that is ⅓ the distance between C0 and C1, luminance values in sub-range III are mapped to a point in color range 25 that is ⅔ the distance between C0 and C1, and luminance values in sub-range IV are mapped to C1. Consequently, because a set of color component values is associated with each luminance value, in effect each set of color component values in block 10 (FIG. 1) is mapped to a point in color range 25. In the present embodiment, there are 4 points in color range 25 and so each point can be represented using 2 bits.


Thus, according to embodiments of the present invention, a set of color component values (a tuple or RGB-tuple) for a texel is used to compute a luminance value, and that luminance value is used to map the set of color component values to an index value. More specifically, the luminance value is compared to a range of luminance values to generate a comparison vector, which in turn is mapped directly to an index value.


In one embodiment, comparison vectors 22 are mapped to index values 28 as follows. A pair of 16-bit palette values, P0 and P1, are computed using C0 and C1 as: P0=C0red*2048+C0green*32+C0blue and P1=C1red*2048+C1green*32+C1blue (that is, the palette values P0 and P1 represent the 16-bit value stored in the RGB565 colors C0 and C1).


In one such embodiment, when the value of P0 is greater than or equal to the value of P1, the values of C0 and C1 are exchanged and the comparison vectors 22 are mapped to the index values 28 using the following table:
















Comparison
Index



Vector 22
Value 28









000
01



100
11



110
10



111
00










In one such embodiment, when the value of P1 is greater than the value of P0, the comparison vectors 22 are mapped to the index values 28 using the following table:
















Comparison
Index



Vector 22
Value 28









000
00



100
10



110
11



111
01











There is no change to the values C0 and C1 when P1 is greater than P0.


Thus, the 16 sets of color component values (tuples) in block 10 (FIG. 1) are encoded using C0 and C1 (16 bits each in RGB565 format) and 16 2-bit index values 28. In the present embodiment, a set of color component values encoded as index value (00) is decoded as C0; a set of color component values encoded as index value (10) is decoded as (2C0+01)/3; a set of color component values encoded as index value (11) is decoded as (C0+2C1)/3; and a set of color component values encoded as index value (01) is decoded as C1.


Significantly, the results of an encoding process in accordance with embodiments of the present invention are similar in type and format (though not in content) to the results of conventional encoding processes such as S3TC. That is, for example, both an encoding process in accordance with embodiments of the present invention and a conventional process such as S3TC result in 2 16-bit colors C0 and C1 and 16 2-bit index values, although the values of C0 and C1 and the index values are determined differently and thus are expected to be numerically different as well. Hence, from the point of view of a decoder device, the encoding process is transparent, and thus the encoding process of the present invention is compatible with legacy decoders.


Generally speaking, instead of mapping colors to index values in three dimensions (red, green and blue) of color space, embodiments in accordance with the present invention map colors to index values in one-dimensional luminance space. Furthermore, luminance values are used to select the representative colors C0 and C1, and the representative colors C0 and C1 are selected from a set of colors (that is, they are members of the set of colors and are not calculated from the set of colors). Accordingly, compression performed in accordance with embodiments of the present invention requires less sophisticated processing relative to conventional processes such as S3TC. Consequently, embodiments in accordance with the present invention are less costly in terms of both area and power consumption than conventional processes, and thus may be better suited for use on devices such as portable (e.g., handheld) electronic devices. Also, embodiments in accordance with the present invention can be readily implemented in either hardware or software. In general, embodiments of the present invention provide a less complex compression scheme that, for the human visual system, also results in acceptable image quality.


Although described for texel data, embodiments in accordance with the present invention are not so limited. For example, embodiments in accordance with the present invention can be implemented in pixel shaders or the like. Also, although described for a single block of texel data, the processes described herein can be repeated for multiple blocks of texel data. Moreover, different blocks of texel data can be processed in parallel. Also, one part of a processing pipeline can be operating on one block of texel data while another part of a processing pipeline operates on another block of texel data.


Furthermore, although described for the compression of raw or natural data, embodiments in accordance with the present invention are not so limited. For example, compressed data may be read from a frame buffer, decompressed and used in some manner. Embodiments in accordance with the present invention can be used to recompress the data before it is read back into storage. In general, because the encoded output produced according to embodiments of the present invention is compatible with conventional encoding processes and decoders, and because embodiments in accordance with the present invention can operate on the same type of input data as conventional processes, embodiments in accordance with the present invention can be transparently substituted for a conventional process anywhere compression is performed.



FIG. 3 is a flowchart 300 of a computer-implemented method for processing (e.g., compressing or encoding) data according to one embodiment of the present invention.


In block 31, data that includes sets of color component values (each set a tuple describing a color) is accessed. In one embodiment, the data is image-based. In one such embodiment, the data includes texel data for a block of texels. In one particular embodiment, the data includes a 4×4 block of texels, and associated with each texel is a set of red, green and blue color component values (an RGB-tuple).


In block 32, in one embodiment, the red, green and blue color component values are represented using 8 bits each. In such an embodiment, the number of bits are reduced by truncating at least 2 least significant bits from each of the 8-bit values.


In block 33, a luminance value is determined from each set of color component values. Thus, a range of luminance values is determined.


In block 34, the minimum and maximum luminance values in the range of luminance values are identified.


In block 35, a first tuple or first set of color component values—from which the minimum luminance value was determined—is selected as the representative color C0, and a second tuple or second set of color component values—from which the maximum luminance value was determined—is selected as the representative color C1. On one embodiment, the colors C0 and C1 are truncated to RGB565 format.


In block 36, each set of color component values is encoded based on where its respective luminance value lies in the range of luminance values that was determined in block 33. In one embodiment, an index value is associated with each set of color component values, depending on where its respective luminance value lies in the range of luminance values. The index value in essence identifies how, during decoding, the representative colors C0 and C1 (described by the aforementioned first and second sets of color component values, respectively) are to be used to reconstruct the encoded sets of color component values. More specifically, during decoding, sets of color component values are interpolated from the representative first and second sets of color component values, and the index values identify how the interpolation is to be performed.


In one embodiment, different thresholds are identified in the range of luminance values, effectively demarcating the range into a number of contiguous sub-ranges. The various luminance values are compared to the threshold values to determine which of the sub-ranges they lie within. In one such embodiment, a unique comparison vector is associated with each of the sub-ranges. The comparison vectors are mapped to a corresponding index value.



FIG. 4 is a flowchart 400 of a computer-implemented method for processing (e.g., decompressing or decoding) data according to one embodiment of the present invention.


In block 41, data that includes an encoded version of sets of color component values for a block of texels is accessed. The encoded version includes a first set of color component values, corresponding to representative color C0, and a second set of color component values, corresponding to representative color C1. The encoded version also includes index values associated with the texels. The encoded version and the index values are determined using the method of encoding data described above in conjunction with FIGS. 1, 2 and 3.


In block 42 of FIG. 4, the representative colors C0 and C1 and an index value associated with a texel are used to decode a third set of color component values that describes a color for the texel. The index value indicates how to interpolate the third set of color component values using C0 and C1. In one embodiment, the third set is decoded as C0 if the index value has a first value, as C1 if the index value has a second value, as (C0+2C1)/3 if the index value has a third value, and as (2C0+C1)/3 if the index value has a fourth value.


Although specific steps are disclosed in flowcharts 300 and 400 of FIGS. 3 and 4, such steps are exemplary. That is, the present invention is well-suited to performing various other steps or variations of the steps recited in flowcharts 300 and 400. It is appreciated that the steps in flowcharts 300 and 400 may be performed in an order different than presented and that the steps in flowcharts 300 and 400 are not necessarily performed in the sequence illustrated.



FIG. 5 is a block diagram of a system 500 upon which an encoding process in accordance with the present invention, such as that described in flowchart 300 (FIG. 3), can be implemented. In the example of FIG. 5, the system 500 includes a host central processing unit (CPU) 51 coupled to a media (e.g., video or graphics) processor unit (MPU) 52 via a bus 55; alternatively, host CPU 51 provides the functionality provided by MPU 52. Both the CPU 51 and the MPU 52 are coupled to a memory 54 via bus 55. In the system 500 embodiment, the memory 54 is a shared memory, whereby the memory 54 stores instructions and data for both the CPU 51 and the MPU 52. Alternatively, there may be separate memories dedicated to the CPU 51 and MPU 52, respectively. The memory 54 can also include a video frame buffer for storing data (e.g., texel data) that drives a coupled display 53.


As shown in FIG. 5, system 500 includes the basic components of a computer system platform that implements functionality in accordance with embodiments of the present invention. System 500 can be implemented as, for example, a number of different types of portable handheld electronic devices. Such devices can include, for example, portable phones, personal digital assistants (PDAs), handheld gaming devices, or virtually any other type of device with display capability. In such embodiments, components can be included that are designed to add peripheral buses, specialized communications components, support for specialized input/output (I/O) devices, and the like.


Embodiments of the present invention are thus described. While the present invention has been described in particular embodiments, it should be appreciated that the present invention should not be construed as limited by such embodiments, but rather construed according to the below claims.

Claims
  • 1. A method of compressing texel data, said method comprising: accessing data comprising sets of color component values for a plurality of texels, each of said sets describing a color;selecting a first set of color component values and a second set of color component values from said sets, wherein said first and second sets define endpoints of a range of colors; andencoding a third set of color component values by associating said third set with an index value, wherein said third set is decodable using said first and second sets and said index value.
  • 2. The method of claim 1 wherein each of said color component values is represented using a number of bits and further comprising reducing said number of bits prior to said selecting.
  • 3. The method of claim 1 wherein said selecting comprises determining a range of luminance values using said sets of color component values, wherein said first set corresponds to a first endpoint of said range of luminance values and said second set corresponds to a second endpoint of said range of luminance values.
  • 4. The method of claim 3 wherein said encoding comprises determining a luminance value for said third set, wherein said luminance value is between said first and second endpoints of said range of luminance values and wherein said index value is selected according to where said luminance value lies in said range of luminance values.
  • 5. The method of claim 3 wherein said plurality of luminance values comprises a luminance value for each of said sets of color component values and further comprising determining a luminance value for a set of color component values by computing a dot product of color component values in said set.
  • 6. The method of claim 5 further comprising computing said dot product as 0.299R+0.587G+0.114B where R, G and B are red, green and blue component values in said set.
  • 7. The method of claim 5 further comprising computing said dot product by: shifting a red value in said set one bit left to produce a shifted red value;shifting a green value in said set two bits left to produce a shifted green value; andadding said shifted red value and said shifted green value to a blue value in said set.
  • 8. A method of compressing image-based data, said method comprising: accessing data comprising a plurality of sets of color component values;determining a plurality of luminance values using said plurality of sets of color component values;identifying a minimum and a maximum of said luminance values, wherein said minimum and maximum define endpoints of a range of luminance values, and wherein one endpoint of said range corresponds to a first set of color component values and the other endpoint of said range corresponds to a second set of color component values and; andencoding a third set of color component values in said plurality by associating an index value with said third set, wherein said third set is decodable using said first and second sets and said index value.
  • 9. The method of claim 8 wherein each of said color component values is represented using a number of bits and further comprising reducing said number of bits prior to said selecting.
  • 10. The method of claim 8 wherein said plurality of luminance values comprises a luminance value for each of said sets of color component values and further comprising computing a dot product of color component values in a set of color component values to determine a luminance value for said set.
  • 11. The method of claim 10 further comprising computing said dot product as 0.299R+0.587G+0.1146 where R, G and B are red, green and blue component values in said set.
  • 12. The method of claim 10 further comprising computing said dot product by: shifting a red value in said set one bit left to produce a shifted red value;shifting a green value in said set two bits left to produce a shifted green value; andadding said shifted red and green values in said set to a blue value in said set.
  • 13. The method of claim 8 wherein said first and second sets of color component values are selected from said plurality of sets of color component values.
  • 14. A method of processing image-based data, said method comprising: accessing data comprising a plurality of sets of color component values;determining a plurality of luminance values using said plurality of sets of color component values;identifying first and second endpoints of a range of said plurality of luminance values, wherein said first endpoint corresponds to a minimum of said luminance values that is determined using a first set of color component values and wherein said second endpoint corresponds to a maximum of said luminance values that is determined using a second set of color component values; andassociating an index value with a third set of color component values, wherein said index value is selected by determining where a luminance value for said third set lies in said range of luminance values.
  • 15. The method of claim 14 wherein each of said color component values is represented using a number of bits and further comprising reducing said number of bits prior to said selecting.
  • 16. The method of claim 14 wherein said plurality of luminance values comprises a luminance value for each of said sets of color component values and further comprising determining a luminance value for a set of color component values by computing a dot product of color component values in said set.
  • 17. The method of claim 16 wherein said dot product is computed for said each set of color component values as 0.299R+0.587G+0.1146 where R, G and B are red, green and blue component values in said set.
  • 18. The method of claim 16 further comprising computing said dot product by: shifting a red value in said set one bit left to produce a shifted red value;shifting a green value in said set two bits left to produce a shifted green value; andadding said shifted red and green values in said set to a blue value in said set.
  • 19. The method of claim 14 further comprising: identifying thresholds within said range, wherein said thresholds demarcate sub-ranges of luminance values within said range and wherein each of said sub-ranges is associated with a respective index value; andcomparing said luminance value for said third set to said thresholds to identify a sub-range that includes said luminance value.
  • 20. The method of claim 14 wherein said first and second sets are selected from said plurality of sets of color component values, wherein said third set is decodable using said first and second sets and said index value.
US Referenced Citations (226)
Number Name Date Kind
3904818 Kovac Sep 1975 A
4253120 Levine Feb 1981 A
4646251 Hayes et al. Feb 1987 A
4682664 Kemp Jul 1987 A
4685071 Lee Aug 1987 A
4739495 Levine Apr 1988 A
4771470 Geiser et al. Sep 1988 A
4803477 Miyatake et al. Feb 1989 A
4920428 Lin et al. Apr 1990 A
4987496 Greivenkamp, Jr. Jan 1991 A
5175430 Enke et al. Dec 1992 A
5227789 Barry et al. Jul 1993 A
5261029 Abi-Ezzi et al. Nov 1993 A
5305994 Matsui et al. Apr 1994 A
5338901 Dietrich Aug 1994 A
5387983 Sugiura et al. Feb 1995 A
5414824 Grochowski May 1995 A
5475430 Hamada et al. Dec 1995 A
5513016 Inoue Apr 1996 A
5608824 Shimizu et al. Mar 1997 A
5652621 Adams, Jr. et al. Jul 1997 A
5736987 Drucker et al. Apr 1998 A
5793371 Deering Aug 1998 A
5793433 Kim et al. Aug 1998 A
5822452 Tarolli et al. Oct 1998 A
5831625 Rich et al. Nov 1998 A
5831640 Wang et al. Nov 1998 A
5835097 Vaswani et al. Nov 1998 A
5841442 Einkauf et al. Nov 1998 A
5878174 Stewart et al. Mar 1999 A
5892517 Rich Apr 1999 A
5903273 Mochizuki et al. May 1999 A
5963984 Garibay, Jr. et al. Oct 1999 A
5995109 Goel et al. Nov 1999 A
6016474 Kim et al. Jan 2000 A
6052127 Vaswani et al. Apr 2000 A
6078331 Pulli et al. Jun 2000 A
6078334 Hanaoka et al. Jun 2000 A
6111988 Horowitz et al. Aug 2000 A
6118547 Tanioka Sep 2000 A
6128000 Jouppi et al. Oct 2000 A
6141740 Mahalingaiah et al. Oct 2000 A
6151457 Kawamoto Nov 2000 A
6175430 Ito Jan 2001 B1
6184893 Devic et al. Feb 2001 B1
6236405 Schilling et al. May 2001 B1
6252611 Kondo Jun 2001 B1
6256038 Krishnamurthy Jul 2001 B1
6281931 Tsao et al. Aug 2001 B1
6289103 Sako et al. Sep 2001 B1
6298169 Guenter Oct 2001 B1
6314493 Luick Nov 2001 B1
6319682 Hochman Nov 2001 B1
6323934 Enomoto Nov 2001 B1
6339428 Fowler et al. Jan 2002 B1
6392216 Peng-Tan May 2002 B1
6396397 Bos et al. May 2002 B1
6438664 McGrath et al. Aug 2002 B1
6469707 Voorhies Oct 2002 B1
6486971 Kawamoto Nov 2002 B1
6504952 Takemura et al. Jan 2003 B1
6549997 Kalyanasundharam Apr 2003 B2
6556311 Benear et al. Apr 2003 B1
6584202 Montag et al. Jun 2003 B1
6594388 Gindele et al. Jul 2003 B1
6683643 Takayama et al. Jan 2004 B1
6707452 Veach Mar 2004 B1
6724932 Ito Apr 2004 B1
6737625 Baharav et al. May 2004 B2
6760080 Moddel et al. Jul 2004 B1
6785814 Usami et al. Aug 2004 B1
6806452 Bos et al. Oct 2004 B2
6819793 Reshetov et al. Nov 2004 B1
6839062 Aronson et al. Jan 2005 B2
6839813 Chauvel Jan 2005 B2
6856441 Zhang et al. Feb 2005 B2
6859208 White Feb 2005 B1
6876362 Newhall, Jr. et al. Apr 2005 B1
6883079 Priborsky Apr 2005 B1
6891543 Wyatt May 2005 B2
6900836 Hamilton, Jr. May 2005 B2
6940511 Akenine-Moller et al. Sep 2005 B2
6950099 Stollnitz et al. Sep 2005 B2
7009639 Une et al. Mar 2006 B1
7015909 Morgan, III et al. Mar 2006 B1
7023479 Hiramatsu et al. Apr 2006 B2
7081898 Sevigny Jul 2006 B2
7082508 Khan et al. Jul 2006 B2
7088388 MacLean et al. Aug 2006 B2
7092018 Watanabe Aug 2006 B1
7106368 Daiku et al. Sep 2006 B2
7107441 Zimmer et al. Sep 2006 B2
7116335 Pearce et al. Oct 2006 B2
7120715 Chauvel et al. Oct 2006 B2
7133041 Kaufman et al. Nov 2006 B2
7133072 Harada Nov 2006 B2
7146041 Takahashi Dec 2006 B2
7221779 Kawakami et al. May 2007 B2
7227586 Finlayson et al. Jun 2007 B2
7236649 Fenney Jun 2007 B2
7245319 Enomoto Jul 2007 B1
7305148 Spampinato et al. Dec 2007 B2
7343040 Chanas et al. Mar 2008 B2
7397946 Reshetov et al. Jul 2008 B2
7447869 Kruger et al. Nov 2008 B2
7486844 Chang et al. Feb 2009 B2
7502505 Malvar et al. Mar 2009 B2
7519781 Wilt Apr 2009 B1
7545382 Montrym et al. Jun 2009 B1
7580070 Yanof et al. Aug 2009 B2
7627193 Alon et al. Dec 2009 B2
7671910 Lee Mar 2010 B2
7728880 Hung et al. Jun 2010 B2
7750956 Wloka Jul 2010 B2
7760936 King et al. Jul 2010 B1
7912279 Hsu et al. Mar 2011 B2
8049789 Innocent Nov 2011 B2
8238695 Davey et al. Aug 2012 B1
20010001234 Addy et al. May 2001 A1
20010012113 Yoshizawa et al. Aug 2001 A1
20010012127 Fukuda et al. Aug 2001 A1
20010015821 Namizuka et al. Aug 2001 A1
20010019429 Oteki et al. Sep 2001 A1
20010021278 Fukuda et al. Sep 2001 A1
20010033410 Helsel et al. Oct 2001 A1
20010050778 Fukuda et al. Dec 2001 A1
20010054126 Fukuda et al. Dec 2001 A1
20020012131 Oteki et al. Jan 2002 A1
20020015111 Harada Feb 2002 A1
20020018244 Namizuka et al. Feb 2002 A1
20020027670 Takahashi et al. Mar 2002 A1
20020033887 Hieda et al. Mar 2002 A1
20020041383 Lewis, Jr. et al. Apr 2002 A1
20020044778 Suzuki Apr 2002 A1
20020054374 Inoue et al. May 2002 A1
20020063802 Gullichsen et al. May 2002 A1
20020105579 Levine et al. Aug 2002 A1
20020126210 Shinohara et al. Sep 2002 A1
20020146136 Carter, Jr. Oct 2002 A1
20020149683 Post Oct 2002 A1
20020158971 Daiku et al. Oct 2002 A1
20020167202 Pfalzgraf Nov 2002 A1
20020167602 Nguyen Nov 2002 A1
20020169938 Scott et al. Nov 2002 A1
20020172199 Scott et al. Nov 2002 A1
20020191694 Ohyama et al. Dec 2002 A1
20020196470 Kawamoto et al. Dec 2002 A1
20030035100 Dimsdale et al. Feb 2003 A1
20030067461 Fletcher et al. Apr 2003 A1
20030122825 Kawamoto Jul 2003 A1
20030142222 Hordley Jul 2003 A1
20030146975 Joung et al. Aug 2003 A1
20030167420 Parsons Sep 2003 A1
20030169353 Keshet et al. Sep 2003 A1
20030169918 Sogawa Sep 2003 A1
20030197701 Teodosiadis et al. Oct 2003 A1
20030222995 Kaplinsky et al. Dec 2003 A1
20030223007 Takane Dec 2003 A1
20040001061 Stollnitz et al. Jan 2004 A1
20040001234 Curry et al. Jan 2004 A1
20040032516 Kakarala Feb 2004 A1
20040051716 Sevigny Mar 2004 A1
20040066970 Matsugu Apr 2004 A1
20040100588 Hartson et al. May 2004 A1
20040101313 Akiyama May 2004 A1
20040109069 Kaplinsky et al. Jun 2004 A1
20040151372 Reshetov et al. Aug 2004 A1
20040189875 Zhai et al. Sep 2004 A1
20040207631 Fenney et al. Oct 2004 A1
20040218071 Chauville et al. Nov 2004 A1
20040247196 Chanas et al. Dec 2004 A1
20050007378 Grove Jan 2005 A1
20050007477 Ahiska Jan 2005 A1
20050030395 Hattori Feb 2005 A1
20050046704 Kinoshita Mar 2005 A1
20050073591 Ishiga et al. Apr 2005 A1
20050099418 Cabral et al. May 2005 A1
20050110790 D'Amora May 2005 A1
20050111110 Matama May 2005 A1
20050185058 Sablak Aug 2005 A1
20050238225 Jo et al. Oct 2005 A1
20050243181 Castello et al. Nov 2005 A1
20050248671 Schweng Nov 2005 A1
20050261849 Kochi et al. Nov 2005 A1
20050268067 Lee et al. Dec 2005 A1
20050286097 Hung et al. Dec 2005 A1
20060004984 Morris et al. Jan 2006 A1
20060050158 Irie Mar 2006 A1
20060061658 Faulkner et al. Mar 2006 A1
20060087509 Ebert et al. Apr 2006 A1
20060133697 Uvarov et al. Jun 2006 A1
20060153441 Li Jul 2006 A1
20060176375 Hwang et al. Aug 2006 A1
20060197664 Zhang et al. Sep 2006 A1
20060259732 Traut et al. Nov 2006 A1
20060259825 Cruickshank et al. Nov 2006 A1
20060274171 Wang Dec 2006 A1
20060290794 Bergman et al. Dec 2006 A1
20060293089 Herberger et al. Dec 2006 A1
20070073996 Kruger et al. Mar 2007 A1
20070091188 Chen et al. Apr 2007 A1
20070106874 Pan et al. May 2007 A1
20070126756 Glasco et al. Jun 2007 A1
20070147706 Sasaki et al. Jun 2007 A1
20070157001 Ritzau Jul 2007 A1
20070168634 Morishita et al. Jul 2007 A1
20070168643 Hummel et al. Jul 2007 A1
20070171288 Inoue et al. Jul 2007 A1
20070236770 Doherty et al. Oct 2007 A1
20070247532 Sasaki Oct 2007 A1
20070262985 Watanabe et al. Nov 2007 A1
20070285530 Kim et al. Dec 2007 A1
20080030587 Helbing Feb 2008 A1
20080043024 Schiwietz et al. Feb 2008 A1
20080062164 Bassi et al. Mar 2008 A1
20080101690 Hsu et al. May 2008 A1
20080143844 Innocent Jun 2008 A1
20080263284 da Silva et al. Oct 2008 A1
20090010539 Guarnera et al. Jan 2009 A1
20090037774 Rideout et al. Feb 2009 A1
20090116750 Lee et al. May 2009 A1
20090128575 Liao et al. May 2009 A1
20090160957 Deng et al. Jun 2009 A1
20090257677 Cabral et al. Oct 2009 A1
20090297022 Pettigrew et al. Dec 2009 A1
20100266201 Cabral et al. Oct 2010 A1
Foreign Referenced Citations (39)
Number Date Country
1275870 Dec 2000 CN
0392565 Oct 1990 EP
1447977 Aug 2004 EP
1550980 Jul 2005 EP
2045026 Oct 1980 GB
2363018 May 2001 GB
61187467 Aug 1986 JP
62151978 Jul 1987 JP
07015631 Jan 1995 JP
8036640 Feb 1996 JP
08079622 Mar 1996 JP
09233353 Sep 1997 JP
2000516752 Dec 2000 JP
2001052194 Feb 2001 JP
2002207242 Jul 2002 JP
2003085542 Mar 2003 JP
2004221838 Aug 2004 JP
2005094048 Apr 2005 JP
2005182785 Jul 2005 JP
2005520442 Jul 2005 JP
2006086822 Mar 2006 JP
2006094494 Apr 2006 JP
2006121612 May 2006 JP
2006134157 May 2006 JP
20060203841 Aug 2006 JP
2007019959 Jan 2007 JP
2007148500 Jun 2007 JP
2007233833 Sep 2007 JP
2007282158 Oct 2007 JP
2008085388 Apr 2008 JP
2008113416 May 2008 JP
2008277926 Nov 2008 JP
2009021962 Jan 2009 JP
1020040043156 May 2004 KR
1020060068497 Jun 2006 KR
1020070004202 Jan 2007 KR
03043308 May 2003 WO
2004063989 Jul 2004 WO
2007093864 Aug 2007 WO
Non-Patent Literature Citations (86)
Entry
Chaudhuri, “The impact of NACKs in shared memory scientific applications”, Feb. 2004, IEEE, IEEE Transactions on Parallel and distributed systems vol. 15, No. 2, p. 134-150.
Laibinis, “Formal Development of Reactive Fault Tolerant Systems”, Sep. 9, 2005, Springer, Second International Workshop, RISE 2005, p. 234-249.
Wikipedia, Memory Address, Oct. 29, 2010, pp. 1-4, www.wikipedia.com.
Wikipedia, Physical Address, Apr. 17, 2010, pp. 1-2, www.wikipedia.com.
Non-Final Office Action, Mailed May 11, 2010; U.S. Appl. No. 11/591,685.
Final Office Action, Mailed Oct. 27, 2010; U.S. Appl. No. 11/591,685.
Restriction, Mailed Apr. 28, 2009; U.S. Appl. No. 11/592,076.
Notice of Allowance, Mailed Jan. 29, 2010; U.S. Appl. No. 11/592,076.
Notice of Allowance, Mailed May 4, 2010; U.S. Appl. No. 11/592,076.
Notice of Allowance, Mailed Aug. 13, 2010; U.S. Appl. No. 11/592,076.
Notice of Allowance, Mailed Nov. 26, 2010; U.S. Appl. No. 11/592,076.
Notice of Restriction, Mailed Aug. 10, 2010; U.S. Appl. No. 12/650,068.
Final Office Action, Mailed Mar. 16, 2010; U.S. Appl. No. 11/523,830.
Notice of Allowance, Mailed Jun. 28, 2010; U.S. Appl. No. 11/523,830.
Notice of Allowance, Mailed Sep. 2, 2010; U.S. Appl. No. 11/523,830.
Notice of Allowance, Mailed Jan. 20, 2011; U.S. Appl. No. 11/523,830.
Notice of Allowance, Mailed May 5, 2011; U.S. Appl. No. 11/523,830.
Final Office Action, Mailed Aug. 1, 2010; U.S. Appl. No. 11/586,756.
Notice of Allowance, Mailed Dec. 27, 2010; U.S. Appl. No. 11/586,756.
Notice of Allowance, Mailed May 12, 2011; U.S. Appl. No. 11/586,756.
Office Action, Mailed Apr. 27, 2009; U.S. Appl. No. 11/591,857.
Non-Final Office Action, Mailed Dec. 7, 2010; U.S. Appl. No. 11/591,857.
Notice of Allowance, Mailed Mar. 18, 2011; U.S. Appl. No. 11/591,857.
Final Office Action, Mailed Jun. 25, 2010; U.S. Appl. No. 11/592,106.
Non-Final Office Action, Mailed Dec. 16, 2010; U.S. Appl. No. 11/592,106.
Non-Final Office Action, Mailed Sep. 19, 2008; U.S. Appl. No. 11/523,926.
Final Office Action, Mailed Apr. 10, 2009; U.S. Appl. No. 11/523,926.
Non-Final Office Action, Mailed Oct. 1, 2009; U.S. Appl. No. 11/523,926.
Notice of Allowance; Mailed Mar. 29, 2010; U.S. Appl. No. 11/523,926.
Notice of Allowance; Mailed Jul. 9, 2010; U.S. Appl. No. 11/523,926.
Notice of Allowance; Mailed Oct. 27, 2010; U.S. Appl. No. 11/523,926.
Notice of Allowance; Mailed Jul. 21, 2010; U.S. Appl. No. 11/523,950.
Notice of Allowance; Mailed Nov. 3, 2010; U.S. Appl. No. 11/523,950.
Notice of Allowance, Mailed Jun. 22, 2009; U.S. Appl. No. 11/586,826.
Notice of Allowance, Mailed Dec. 14, 2009; U.S. Appl. No. 11/586,826.
Non-Final Office Action, Mailed May 27, 2010; U.S. Appl. No. 11/591,629.
Non-Final Office Action, Mailed Nov. 3, 2010; U.S. Appl. No. 11/591,629.
Final Office Action, Mailed Jun. 8, 2011; U.S. Appl. No. 11/591,629.
Non-Final Office Action, Mailed Apr. 28, 2010; U.S. Appl. No. 11/592,780.
Non-Final Office Action, Mailed Oct. 13, 2010; U.S. Appl. No. 11/592,780.
Final Office Action, Mailed Apr. 27, 2010; U.S. Appl. No. 11/588,177.
Non-Final Office Action, Mailed Apr. 27, 2010; U.S. Appl. No. 11/591,856.
Notice of Allowance, Mailed Nov. 12, 2010; U.S. Appl. No. 11/591,856.
Notice of Allowance, Mailed Mar. 9, 2011; U.S. Appl. No. 11/591,856.
Non-Final Office Action, Mailed Feb. 22, 2010; U.S. Appl. No. 11/586,825.
Notice of Allowance, Mailed Aug. 16, 2010; U.S. Appl. No. 11/586,825.
Notice of Allowance, Mailed Nov. 26, 2010; U.S. Appl. No. 11/586,825.
Notice of Allowance, Mailed Mar. 4, 2011; U.S. Appl. No. 11/586,825.
Loop, C., DeRose, T., Generalized B-Spline surfaces o arbitrary topology, Aug. 1990, SIGRAPH 90, pp. 347-356.
M. Halstead, M. Kass, T. DeRose; “efficient, fair interolation using catmull-clark surfaces”; Sep. 1993; Computer Graphics and Interactive Techniques, Proc; pp. 35-44.
Morimoto et al., “Fast Electronic Digital Image Stabilization for Off-Road Navigation”, Computer Vision Laboratory, Center for Automated Research University of Maryland, Real-Time Imaging, vol. 2, pp. 285-296, 1996.
Paik et al., “An Adaptive Motion Decision system for Digital Image Stabilizer Based on Edge Pattern Matching”, IEEE Transactions on Consumer Electronics, vol. 38, No. 3, pp. 607-616, Aug. 1992.
Parhami, Computer Arithmetic, Oxford University Press, Jun. 2000, pp. 413-418.
S. Erturk, “Digital Image Stabilization with Sub-Image Phase Correlation Based Global Motion Estimation”, IEEE Transactions on Consumer Electronics, vol. 49, No. 4, pp. 1320-1325, Nov. 2003.
S. Erturk, “Real-Time Digital Image Stabilization Using Kalman Filters”, http://www,ideallibrary.com, Real-Time Imaging 8, pp. 317-328, 2002.
T. DeRose, M., Kass, T. Troung; “subdivision surfaces in character animation”; Jul. 1998; Computer Graphics and Interactive Techniques, Proc; pp. 85-94.
Takeuchi, S., Kanai, T., Suzuki, H., Shimada, K., Kimura, F., Subdivision surface fitting with QEM-basd mesh simplificatio and reconstruction of aproximated B-Spline surfaces, 200, Eighth Pacific Conference on computer graphics and applications pp. 202-2012.
Uomori et al., “Automatic Image Stabilizing System by Full-Digital Signal Processing”, vol. 36, No. 3, pp. 510-519, Aug. 1990.
Uomori et al., “Electronic Image Stabiliztion System for Video Cameras and VCRS”, J. Soc. Motion Pict. Telev. Eng., vol. 101, pp. 66-75, 1992.
“A Pipelined Architecture for Real-Time orrection of Barrel Distortion in Wide-Angle Camera Images”, Hau, T. Ngo, Student Member, IEEE and Vijayan K. Asari, Senior Member IEEE, IEEE Transaction on Circuits and Sytstems for Video Technology: vol. 15 No. Mar. 3, 2005 pp. 436-444.
“Calibration and removal of lateral chromatic abberation in images” Mallon, et al. Science Direct Copyright 2006; 11 pages.
“Method of Color Interpolation in a Singe Sensor Color Camera Using Green Channel Seperation” WEERASIGHE, et al Visual Information Processing Lab, Motorola Austrailian Research Center pp. IV-3233-IV3236, 2002.
D. Doo, M. Sabin “Behaviour of recrusive division surfaces near extraordinary points”; September 197; Computer Aided Design; vol. 10, pp. 356-360, 1970.
D.W.H. Doo; “A subdivision algorithm for smoothing down irregular shaped polyhedrons”; 1978; Interactive Techniques in Computer Aided Design; pp. 157-165.
Davis, J., Marschner, S., Garr, M., Levoy, M., Filling holes in complex surfaces using volumetric diffusion, Dec. 2001, Stanford University, pp. 1-9.
Donald D. Spencer, “Illustrated Computer Graphics Dictionary”, 1993, Camelot Publishing Company, p. 272.
Duca et al., “A Relational Debugging Engine for Graphics Pipeline, International Conference on Computer Graphics and Interactive Techniques”, ACM SIGGRAPH Jul. 2005, pp. 453-463.
E. Catmull, J. Clark, “recursively enerated B-Spline surfaces on arbitrary topological meshes”; Nov. 1978; Computer aided design; vol. 10; pp. 350-355.
gDEBugger, graphicRemedy, http://www.gremedy.com, Aug. 8, 2006, pp. 1-18.
http://en.wikipedia.org/wiki/Bayer—filter; “Bayer Filter”; Wikipedia, the free encyclopedia; pp. 1-4.
http://en.wikipedia.org/wiki/Colorfilter—array; “Color Filter Array”; Wikipedia, the free encyclopedia; pp. 1-5.
http://en.wikipedia.org/wiki/Color—space; “Color Space”; Wikipedia, the free encyclopedia; pp. 1-4.
http://en.wikipedia.org/wiki/Color—translation; “Color Management”; Wikipedia, the free encyclopedia; pp. 1-4.
http://en.wikipedia.org/wiki/Demosaicing; “Demosaicing”; Wikipedia, the free encyclopedia; pp. 1-5.
http://en.wikipedia.org/wiki/Half—tone; “Halftone”; Wikipedia, the free encyclopedia; pp. 1-5.
http://en.wikipedia.org/wiki/L*a*b*; “Lab Color Space”; Wikipedia, the free encyclopedia; pp. 1-4.
http://Slashdot.org/articles/07/09/06/1431217.html.
http:englishrussia.com/?p=1377.
J. Bolz, P. Schroder; “rapid evaluation of catmull-clark subdivision surfaces”; Web 3D '02.
J. Stam; “Exact Evaluation of Catmull-clark subdivision surfaces at arbitrary parameter values”; Jul. 1998; Computer Graphics; vol. 32; pp. 395-404.
Keith R. Slavin; Application As Filed entitled “Efficient Method for Reducing Noise and Blur in a Composite Still Image From a Rolling Shutter Camera”; U.S. Appl. No. 12/069,669, filed Feb. 11, 2008.
Ko et al., “Fast Digital Image Stabilizer Based on Gray-Coded Bit-Plane Matching”, IEEE Transactions on Consumer Electronics, vol. 45, No. 3, pp. 598-603, Aug. 1999.
Ko, et al., “Digital Image Stabilizing Algorithms Basd on Bit-Plane Matching”, IEEE Transactions on Consumer Electronics, vol. 44, No. 3, pp. 617-622, Aug. 1988.
Krus, M., Bourdot, P., Osorio, A., Guisnel, F., Thibault, G., Adaptive tessellation of connected primitives for interactive walkthroughs in complex industrial virtual environments, Jun. 1999, Proceedings of the Eurographics workshop, pp. 1-10.
Kumar, S., Manocha, D., Interactive display of large scale trimmed NURBS models, 1994, University of North Carolina at Chapel Hill, Technical Report, p. 1-36.
Kuno et al. “New Interpolation Method Using Discriminated Color Correlation for Digital Still Cameras” IEEE Transac. On Consumer Electronics, vol. 45, No. 1, Feb. 1999, pp. 259-267.