Patent Application
20160295234
The present invention is generally directed to video processing, and in particular to video compression.
Video encoders apply motion-compensated prediction in order to reduce the amount of image data that must be encoded. This is done by exploiting temporal correlation between successive frames. For example, if a video shows an object moving against a stationary background, only the information representing the moving object needs to be encoded once the information representing the background has been obtained. Motion of the object between a reference frame and a frame currently being encoded is described by motion vectors.
Motion-compensated prediction or motion estimation (ME) includes finding, for each possible pixel block size of a current frame, the “best-possible” match among blocks within a previously encoded frame called a reference frame. Most encoders measure distortion induced by choosing a certain block as a predictor. The “best-possible” match is chosen by minimizing a distortion value subject to a bitrate budget. Since distortion tends to increase as bitrate increases, finding the “best-possible” match subject to a bitrate budget is referred to as rate-distortion (RD) optimization.
Highly accurate ME algorithms are prohibitively expensive in terms of computational complexity and memory bandwidth. The complexity of ME has even increased with the recent High Efficiency Video Encoding (HEVC) standard, which allows prediction block sizes from 4×8 pixels up to 64×64 pixels, whereas previous commonly used standards often used blocks of 8×8 pixels. Since searching for the best match for every possible block size involves redundant computations, practical implementations of software and/or hardware video encoders store distortion values of smaller blocks (e.g., 4×8, 8×8 and 16×16) to re-use them when evaluating RD costs of bigger blocks (e.g., 32×32 and 64×64).
Such merge-based strategies offer the advantage of providing accurate motion estimations at a low computational complexity and memory bandwidth costs. However, these advantages are obtained at the cost of high storage requirements, since distortion values need to be stored for every possible combination of a block size and motion vector within the search area.
Disclosed is a low-complexity and yet efficient method to compress distortion information for motion estimation, resulting in significant reduction in needed storage capacity. A system for implementing the method and a computer-readable medium for storing the method are also disclosed. The method, in one embodiment, includes determining and storing a distortion value for each trial motion vector in a plurality of trial motion vectors. Each trial motion vector specifies a position of a search region relative to a reference frame. The method further includes compressing each of the distortion values as a fixed number of bits based upon a minimum distortion value amongst the stored distortion values, and re-storing each compressed distortion value in place of its uncompressed value.
A more detailed understanding may be had from the following description, given by way of example in conjunction with the accompanying drawings wherein:
Once a distortion value for each trial motion vector has been determined and stored, a minimum distortion value among the stored distortion values is determined 125. Then, each of the stored distortion values is compressed as a fixed number of bits, or using a fixed number of bits, the compression using the minimum distortion value. Each compressed distortion value is then re-stored, in place of its uncompressed value 130. In this way, the amount of storage space needed to store the distortion values for different trial motion vectors maybe significantly reduced.
A distortion value may be determined using any of numerous known methods, including, but not limited to, sum of absolute differences (SAD), sum of absolute transformed differences (SATD) or sum of square errors (SSE). In general, the distortion measures exhibit the following properties:
1) They are positive integers or zero;
2) They have high spatial correlations—that is, for a given block, neighboring motion vectors have close distortion values; and
3) Trial motion vectors associated with low distortion values of smaller blocks have a higher probability to be chosen.
In such methods, pixel values in the search region are compared with pixel values in a corresponding region in the reference frame. Numbers representing these comparisons are then combined into a single number—a distortion value—that acts as a measure of how similar or how different the two groups of pixel values are. In an embodiment, compressing and decoding the distortion values as a fixed number of bits 230 may be done using methods that require only add, shift, and bitwise AND operations, thereby simplifying and speeding up the execution of the method. Such methods may be suitable for on-the-fly encoding and decoding. As an example of such a method, compressing a distortion value may be done by determining and storing two binary integers p and q, both having a user-defined bit length, such that for all possible such binary integer pairs the expression:
p*2q+a+Dk Equation (1)
is closest to the distortion value being compressed, wherein Dk is the minimum distortion value and s is a fixed compression shift. The compressed distortion value D′ may then be expressed as
D′≈p*2q+s+Dk Equation (2)
where “=” may be read as “is approximated by”. For later computations, a decoded distortion value D may be obtained by retrieving the stored p and q and computing the value
D=p*2q+s+Dk Equation (3)
where “=” is read as “is equal to”. In one specific example of this embodiment, the user-defined bit length of p may be six and of q two, in which the distortion value is compressed as eight bits. By contrast, in the past, non-compressed stored values of SAD or SATD have been represented by 16-24 bits.
An example of an algorithm for compressing a distortion value as in Equation 2 is:
An example of an algorithm for decoding a distortion value as in Equation 3 is:
The accuracy of a motion vector determined using a method such as those described above may be refined using a method shown in
Returning to
In an alternative, a distortion value for each subregion not in the subset, such as subregions 215 in
The processor 402 may include a central processing unit (CPU), a graphics processing unit (GPU), a CPU and GPU located on the same die, or one or more processor cores, wherein each processor core may be a CPU or a GPU. The memory 404 may be located on the same die as the processor 402, or may be located separately from the processor 402. The memory 404 may include a volatile or non-volatile memory, for example, random access memory (RAM), dynamic RAM, or a cache.
The storage device 406 may include a fixed or removable storage, for example, a hard disk drive, a solid state drive, an optical disk, or a flash drive. The input devices 408 may include a video camera, video disc player, or other source of video images, a keyboard, a keypad, a touch screen, a touch pad, a detector, a microphone, an accelerometer, a gyroscope, a biometric scanner, or a network connection (e.g., a wireless local area network card for transmission and/or reception of wireless IEEE 802 signals). The output devices 410 may include a video screen, a display, a speaker, a printer, a haptic feedback device, one or more lights, an antenna, or a network connection (e.g., a wireless local area network card for transmission and/or reception of wireless IEEE 802 signals).
The input driver 412 communicates with the processor 402 and the input devices 408, and permits the processor 402 to receive input from the input devices 408. The output driver 414 communicates with the processor 402 and the output devices 410, and permits the processor 402 to send output to the output devices 410. It is noted that the input driver 412 and the output driver 414 are optional components, and that the system 400 will operate in the same manner if the input driver 412 and the output driver 414 are not present.
System 400 may be configured to perform embodiments of a method of compressing motion estimation information in video processing as described hereinbefore. For example, memory 404 or storage device 406 may be configured to store a search region for a block and a plurality of trial motion vectors. Processor 402 may be configured to determine a distortion value associated with each trial motion vector in the plurality of trial motion vectors, each trial motion vector specifying a position of the search region relative to the block. Processor 402 may be configured to store distortion values in memory 404 or storage device 406. Processor 402 may be configured to determine a minimum distortion value among the stored distortion values and its associated trial motion vector. Processor 402 may be configured to compress each of the distortion values as a fixed number of bits using the minimum distortion value, and re-store each compressed distortion value in memory 404 or storage device 406.
It should be understood that many variations are possible based on the disclosure herein. Although features and elements are described above in particular combinations, each feature or element may be used alone without the other features and elements or in various combinations with or without other features and elements.
The methods provided may be implemented in a general purpose computer, a processor, or a processor core. Suitable processors include, by way of example, a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), and/or a state machine. Such processors may be manufactured by configuring a manufacturing process using the results of processed hardware description language (HDL) instructions and other intermediary data including netlists (such instructions capable of being stored on a computer readable media). The results of such processing may be maskworks that are then used in a semiconductor manufacturing process to manufacture a processor which implements aspects of the present invention.
The methods or flow charts provided herein may be implemented in a computer program, software, or firmware incorporated in a computer-readable storage medium for execution by a general purpose computer or a processor. Examples of computer-readable storage mediums include a read only memory (ROM), a random access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs).
