Patent Application
20230298219
The present embodiments generally relate to a method and an apparatus for encoding and decoding images and video, and more particularly, to a method or an apparatus for efficiently providing video compression and/or decompression based on end-to-end deep learning or deep neural network.
To achieve high compression efficiency, image and video coding schemes usually employ prediction and transform to leverage spatial and temporal redundancy in the video content. Generally, intra or inter prediction is used to exploit the intra or inter picture correlation, then the differences between the original block and the predicted block, often denoted as prediction errors or prediction residuals, are transformed, quantized, and entropy coded. To reconstruct the video, the compressed data are decoded by inverse processes corresponding to the entropy coding, quantization, transform, and prediction.
According to an embodiment, a method of updating a Deep Neural Network-based decoder is provided comprising decoding at least one update parameter and modifying the deep neural network-based decoder based on said decoded update parameter.
According to another embodiment, an apparatus for updating a Deep Neural Network-based decoder is provided, comprising one or more processors, wherein said one or more processors are configured to decode at least one update parameter, and modify the deep neural network-based decoder based on said decoded update parameter.
According to another embodiment, a method for obtaining an update parameter for updating a Deep Neural Network-based decoder is provided, comprising: obtaining at least one update parameter for modifying a deep-neural-network-based decoder defined from a training of a deep neural network-based auto-encoder using a first training configuration, said at least one update parameter being obtained as a function of a training of said deep neural network-based auto-encoder using a second training configuration, and encoding said at least one update parameter.
According to another embodiment, an apparatus for obtaining an update parameter for updating a Deep Neural Network-based decoder is provided, comprising one or more processors, wherein said one or more processors are configured to obtain at least one update parameter for modifying a deep-neural-network-based decoder defined from a training of a deep neural network-based auto-encoder using a first training configuration, said at least one update parameter being obtained as a function of a training of said deep neural network-based auto-encoder using a second training configuration, and encode said at least one update parameter.
One or more embodiments also provide a computer program comprising instructions which when executed by one or more processors cause the one or more processors to perform the methods according to any of the embodiments described below. One or more of the present embodiments also provide a computer readable storage medium having stored thereon instructions for performing the methods according to any of the embodiments described below. One or more embodiments also provide a computer readable storage medium having stored thereon a bitstream generated according to the methods described herein. One or more embodiments also provide a method and apparatus for transmitting or receiving the bitstream generated according to the methods described herein.
These and other aspects, features and advantages of the general aspects will become apparent from the following detailed description of exemplary embodiments, which is to be read in connection with the accompanying drawings.
Some image and video coding schemes employ Deep Neural Networks (DNN) in some or all parts of the coding-decoding schemes.
DNNs are trained using several types of losses: “objective metric” and “subjective” metric. Loss based on an “objective” metric may be typically Mean Squared Error (MSE) or based on structural similarity (SSIM) for instance. The results may not be perceptually as good as the “subjective metric”, but the fidelity to the original signal (image) is higher. Loss based on “subjective” (or subjective by proxy) may be typically using Generative Adversarial Networks (GANs) during the training stage or advanced visual metric via a proxy Neural Network (NN). Depending on the loss used for training, the resulting parameters of the DNN model may be different.
The DNN models are trained using several types of training sets. A same network can be first trained on a generic training set, allowing a satisfactory performance on a large range of content types. The DNN model can also be fine-tuned using a specific training set for a specific usage, improving the performance on a domain specific content. These different trainings will result in different trained models.
Therefore, there is a need for a Deep Neural network (DNN) for compression suitable to run in both objective and perceptual/subjective quality. While objective metrics give visually poorer results, they offer several advantages:
On the other hand, subjective metrics allow for more pleasing results perceptually, especially at low bitrates.
In a same way, a generic training set ensures that compression performance is consistent on a wide range of content, but a specific training set could reach better performances for specific applications. Additionally, auto-encoder solutions may be trained at given rate-points, i.e. the weights of the models are optimized for a specific range of bitrates of the transmitted bitstream.
Current methods using end-to-end compression network usually train a unique network, either for an objective metric (typically MSE/PSNR for Peak Signal to Noise Ratio), or using a perceptual metric, typically using GANs or advanced perceptual metric loss. In traditional codecs, quantization matrices and encoding methods are used to adapt the codec to more perceptually oriented quality or specific content. Typically, carefully chosen non-flat matrices, even if degrading the PSNR, allow to increase the visual quality of reconstructed frames.
According to an embodiment, a network using objective metrics and/or generic training set is trained. Network updates are used to turn the decoder network into a perceptual based decompressor or domain specific decompressor. The updates may be small and fixed, so that an application can optimize the decoding process knowing the decoder architecture and most of the layers are fixed (i.e. weights are known). In practice, a hardware version of the decoder could be implemented and used together with a thin software process for updating the decoder.
According to an embodiment, an auto-encoder (AE) is trained using a first training configuration, for instance using an objective metric such as MSE for “signal” based fidelity of the compression, using a generic training set. Layers are added and/or removed to/from the decoder and/or adapted to change the decoder reconstruction. Both encoder and some layers of the decoder could be updated. The auto-encoder is then re-trained or fine-tuned using another training configuration, for instance using a subjective metric or a specific training set, or for specific bitrates.
A training configuration is defined by a metric used in the loss function, and a training set of samples or batch which are input to the auto-encoder so that the auto-encoder learns its parameters. The other training configuration could differ from the first training configuration from the metric which could be an objective or perceptual/subjective quality metric and/or the training set which could be a generic training set or a training set with specific contents. The training configurations could also differ in the Lagrange parameters for updating or refining in a light way a DNN to adapt to different bitrate levels.
According to another embodiment, multiple decoder outputs are provided, keeping an objective output only in the loop, i.e. in case of temporal prediction. The objective output will be used in the coding loop, while the subjective output could be used for display.
According to another embodiment, syntax elements are sent to the decoder along with the bitstream or as side information, for updating the decoder.
In the following, the description provides exemplary embodiments related to the adaptation of the auto-encoder to perceptual metrics. However, the scope of the disclosure is not limited to perceptual optimization. In particular, videos could also be used for machine tasks, e.g. object tracking, segmentation etc. in different contexts such as self-driving vehicles, video surveillance etc. The model adaptations described below are also applicable in these contexts where the perceptual metric could be replaced by accuracy metrics of a machine task algorithm which takes as input the decompressed video.
The model adaptations described below are also applicable to specialize the coding/decoding framework to some specific type of video content. In this context, the training of one or more modified network layers and the fine tuning of the network may be specifically focused on the considered specific video content type. As an example, video gaming content may be a considered specific content type.
The system 100 includes at least one processor 110 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this application. Processor 110 may include embedded memory, input output interface, and various other circuitries as known in the art. The system 100 includes at least one memory 120 (e.g., a volatile memory device, and/or a non-volatile memory device). System 100 includes a storage device 140, which may include non-volatile memory and/or volatile memory, including, but not limited to, EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic disk drive, and/or optical disk drive. The storage device 140 may include an internal storage device, an attached storage device, and/or a network accessible storage device, as non-limiting examples.
System 100 includes an encoder/decoder module 130 configured, for example, to process data to provide an encoded video or decoded video, and the encoder/decoder module 130 may include its own processor and memory. The encoder/decoder module 130 represents module(s) that may be included in a device to perform the encoding and/or decoding functions. As is known, a device may include one or both of the encoding and decoding modules. Additionally, encoder/decoder module 130 may be implemented as a separate element of system 100 or may be incorporated within processor 110 as a combination of hardware and software as known to those skilled in the art.
Program code to be loaded onto processor 110 or encoder/decoder 130 to perform the various aspects described in this application may be stored in storage device 140 and subsequently loaded onto memory 120 for execution by processor 110. In accordance with various embodiments, one or more of processor 110, memory 120, storage device 140, and encoder/decoder module 130 may store one or more of various items during the performance of the processes described in this application. Such stored items may include, but are not limited to, the input video, the decoded video or portions of the decoded video, the bitstream, matrices, variables, and intermediate or final results from the processing of equations, formulas, operations, and operational logic.
In several embodiments, memory inside of the processor 110 and/or the encoder/decoder module 130 is used to store instructions and to provide working memory for processing that is needed during encoding or decoding. In other embodiments, however, a memory external to the processing device (for example, the processing device may be either the processor 110 or the encoder/decoder module 130) is used for one or more of these functions. The external memory may be the memory 120 and/or the storage device 140, for example, a dynamic volatile memory and/or a non-volatile flash memory. In several embodiments, an external non-volatile flash memory is used to store the operating system of a television. In at least one embodiment, a fast external dynamic volatile memory such as a RAM is used as working memory for video coding and decoding operations, such as for MPEG-2, HEVC, or VVC.
The input to the elements of system 100 may be provided through various input devices as indicated in block 105. Such input devices include, but are not limited to, (i) an RF portion that receives an RF signal transmitted, for example, over the air by a broadcaster, (ii) a Composite input terminal, (iii) a USB input terminal, and/or (iv) an HDMI input terminal.
In various embodiments, the input devices of block 105 have associated respective input processing elements as known in the art. For example, the RF portion may be associated with elements suitable for (i) selecting a desired frequency (also referred to as selecting a signal, or band-limiting a signal to a band of frequencies), (ii) down converting the selected signal, (iii) band-limiting again to a narrower band of frequencies to select (for example) a signal frequency band which may be referred to as a channel in certain embodiments, (iv) demodulating the down converted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets. The RF portion of various embodiments includes one or more elements to perform these functions, for example, frequency selectors, signal selectors, band-limiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers. The RF portion may include a tuner that performs various of these functions, including, for example, down converting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or to baseband. In one set-top box embodiment, the RF portion and its associated input processing element receives an RF signal transmitted over a wired (for example, cable) medium, and performs frequency selection by filtering, down converting, and filtering again to a desired frequency band. Various embodiments rearrange the order of the above-described (and other) elements, remove some of these elements, and/or add other elements performing similar or different functions. Adding elements may include inserting elements in between existing elements, for example, inserting amplifiers and an analog-to-digital converter. In various embodiments, the RF portion includes an antenna.
Additionally, the USB and/or HDMI terminals may include respective interface processors for connecting system 100 to other electronic devices across USB and/or HDMI connections. It is to be understood that various aspects of input processing, for example, Reed-Solomon error correction, may be implemented, for example, within a separate input processing IC or within processor 110 as necessary. Similarly, aspects of USB or HDMI interface processing may be implemented within separate interface ICs or within processor 110 as necessary. The demodulated, error corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 110, and encoder/decoder 130 operating in combination with the memory and storage elements to process the datastream as necessary for presentation on an output device.
Various elements of system 100 may be provided within an integrated housing, Within the integrated housing, the various elements may be interconnected and transmit data therebetween using suitable connection arrangement 115, for example, an internal bus as known in the art, including the 12C bus, wiring, and printed circuit boards.
The system 100 includes communication interface 150 that enables communication with other devices via communication channel 190. The communication interface 150 may include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 190. The communication interface 150 may include, but is not limited to, a modem or network card and the communication channel 190 may be implemented, for example, within a wired and/or a wireless medium.
Data is streamed to the system 100, in various embodiments, using a Wi-Fi network such as IEEE 802.11. The Wi-Fi signal of these embodiments is received over the communications channel 190 and the communications interface 150 which are adapted for Wi-Fi communications. The communications channel 190 of these embodiments is typically connected to an access point or router that provides access to outside networks including the Internet for allowing streaming applications and other over-the-top communications. Other embodiments provide streamed data to the system 100 using a set-top box that delivers the data over the HDMI connection of the input block 105. Still other embodiments provide streamed data to the system 100 using the RF connection of the input block 105.
The system 100 may provide an output signal to various output devices, including a display 165, speakers 175, and other peripheral devices 185. The other peripheral devices 185 include, in various examples of embodiments, one or more of a stand-alone DVR, a disk player, a stereo system, a lighting system, and other devices that provide a function based on the output of the system 100. In various embodiments, control signals are communicated between the system 100 and the display 165, speakers 175, or other peripheral devices 185 using signaling such as AV.Link, CEC, or other communications protocols that enable device-to-device control with or without user intervention. The output devices may be communicatively coupled to system 100 via dedicated connections through respective interfaces 160, 170, and 180. Alternatively, the output devices may be connected to system 100 using the communications channel 190 via the communications interface 150. The display 165 and speakers 175 may be integrated in a single unit with the other components of system 100 in an electronic device, for example, a television. In various embodiments, the display interface 160 includes a display driver, for example, a timing controller (T Con) chip.
The display 165 and speaker 175 may alternatively be separate from one or more of the other components, for example, if the RF portion of input 105 is part of a separate set-top box. In various embodiments in which the display 165 and speakers 175 are external components, the output signal may be provided via dedicated output connections, including, for example, HDMI ports, USB ports, or COMP outputs.
In the present application, the terms “reconstructed” and “decoded” may be used interchangeably, the terms “encoded” or “coded” may be used interchangeably, the terms “pixel” or “sample” may be used interchangeably, and the terms “image,” “picture” and “frame” may be used interchangeably. Usually, but not necessarily, the term “reconstructed” is used at the encoder side while “decoded” is used at the decoder side.
Before being encoded, the video sequence may go through pre-encoding processing (201), for example, applying a color transform to the input color picture (e.g., conversion from RGB 4:4:4 to YCbCr 4:2:0), or performing a remapping of the input picture components in order to get a signal distribution more resilient to compression (for instance using a histogram equalization of one of the color components). Metadata can be associated with the pre-processing, and attached to the bitstream.
In the encoder 200, a picture is encoded by the encoder elements as described below. The picture to be encoded is partitioned (202) and processed in units of, for example, CUs. Each unit is encoded using, for example, either an intra or inter mode. When a unit is encoded in an intra mode, it performs intra prediction (260). In an inter mode, motion estimation (275) and compensation (270) are performed. The encoder decides (205) which one of the intra mode or inter mode to use for encoding the unit, and indicates the intra/inter decision by, for example, a prediction mode flag. The encoder may also blend (263) intra prediction result and inter prediction result, or blend results from different intra/inter prediction methods.
Prediction residuals are calculated, for example, by subtracting (210) the predicted block from the original image block. The motion refinement module (272) uses already available reference picture in order to refine the motion field of a block without reference to the original block. A motion field for a region can be considered as a collection of motion vectors for all pixels with the region. If the motion vectors are sub-block-based, the motion field can also be represented as the collection of all sub-block motion vectors in the region (all pixels within a sub-block has the same motion vector, and the motion vectors may vary from sub-block to sub-block). If a single motion vector is used for the region, the motion field for the region can also be represented by the single motion vector (same motion vectors for all pixels in the region).
The prediction residuals are then transformed (225) and quantized (230). The quantized transform coefficients, as well as motion vectors and other syntax elements, are entropy coded (245) to output a bitstream. The encoder can skip the transform and apply quantization directly to the non-transformed residual signal. The encoder can bypass both transform and quantization, i.e., the residual is coded directly without the application of the transform or quantization processes.
The encoder decodes an encoded block to provide a reference for further predictions. The quantized transform coefficients are de-quantized (240) and inverse transformed (250) to decode prediction residuals. Combining (255) the decoded prediction residuals and the predicted block, an image block is reconstructed. In-loop filters (265) are applied to the reconstructed picture to perform, for example, deblocking/SAO (Sample Adaptive Offset) filtering to reduce encoding artifacts. The filtered image is stored at a reference picture buffer (280).
In particular, the input of the decoder includes a video bitstream, which can be generated by video encoder 200. The bitstream is first entropy decoded (330) to obtain transform coefficients, motion vectors, and other coded information. The picture partition information indicates how the picture is partitioned. The decoder may therefore divide (335) the picture according to the decoded picture partitioning information. The transform coefficients are de-quantized (340) and inverse transformed (350) to decode the prediction residuals. Combining (355) the decoded prediction residuals and the predicted block, an image block is reconstructed.
The predicted block can be obtained (370) from intra prediction (360) or motion-compensated prediction (i.e., inter prediction) (375). The decoder may blend (373) the intra prediction result and inter prediction result, or blend results from multiple intra/inter prediction methods. Before motion compensation, the motion field may be refined (372) by using already available reference pictures. In-loop filters (365) are applied to the reconstructed image. The filtered image is stored at a reference picture buffer (380).
The decoded picture can further go through post-decoding processing (385), for example, an inverse color transform (e.g. conversion from YCbCr 4:2:0 to RGB 4:4:4) or an inverse remapping performing the inverse of the remapping process performed in the pre-encoding processing (201). The post-decoding processing can use metadata derived in the pre-encoding processing and signaled in the bitstream.
According to an embodiment, all or parts of the video encoder and decoder described in reference to
The input I of the encoder part 401 of the network may consist of
The input I may have one or multiple components, e.g monochrome, RGB or YUV components.
The encoder network 401 is usually composed of a set of convolutional layers with stride, allowing to reduce the spatial resolution of the input while increasing the depth, i.e. the number of channels of the input. Squeeze operations may also be used instead of strided convolutional layers (space-to-depth via reshaping and permutations). In the exemplary embodiment illustrated on
The output of the encoder, which consists of a tensor sometimes referred to as a latent in the following, is then quantized, and entropy coded to produce a bitstream b. At training, a so-called “spatial-bottleneck” which reduces the number of values in the latent or an “entropy-bottleneck” to simulate the entropy coding module are used to allow compression of the original data. “b” is called the bitstream, i.e. the set of coded syntax elements and payloads of bins representing the quantized symbols, transmitted to the decoder.
The decoder part 402, after entropy decoding the quantized symbol from the bitstream b, inputs the values to a set of layers usually composed of (de) convolutional layers (or depth-to-space squeeze operations). The output of the decoder 402 is the reconstructed image Î or a group of images.
Note that some more sophisticated layouts exist, for example adding an “hyper-autoencoder” (hyper-prior) to the network in order to jointly learn the latent distribution properties of the encoder output. More details on such auto-encoder can be found in “Joint Autoregressive and hierarchical priors for learned image compression”, D. Minnen, J. Ballé, G. Toderici, NIPS 2018′.
According to an embodiment, the bitstream is transmitted to a decoder for updating the decoder.
At least one part of an image is encoded (502) in a bitstream, using the DNN auto-encoder which has been trained using the second training configuration. According to an embodiment, the bitstream is transmitted to a decoder.
According to an embodiment, another bitstream comprising coded data representative of at least one part of at least one image is received by the decoder. In a variant, the coded data representative of at least one part of at least one image is comprised in the same bitstream as the update parameter. The modified DNN-based decoder then decodes (602) the received data to reconstruct the at least one part of an image.
The auto-encoder (encoder and decoder parts) is trained (700) using an objective metric (typically MSE) and a generic dataset. The loss function also comprises a rate term R which depends on the entropy of the coded latent “b”. A stands for the Lagrangian parameter as it is known in rate-distortion optimization. Once trained in this first configuration, the decoder part of the network is freeze, i.e. the learnable weights of the decoder layers are freeze and stored.
For specific usage, the encoder is then retrained or fine-tuned (701) using another metric for the loss function, typically a “perceptual” metric, or retrain/fine-tune using another domain specific training-set. The “perceptual” metric is represented by the term p(I, Î) on
During the retraining process or fine-tuning, a decoder adaptation is performed. One or more layers are added or removed in the decoder network in addition to the fixed layers already present. According to another variant, an already layer can be adapted. The layer(s) information (update parameter m) is sent to the decoder as part of the bitstream or as side information. The loss function may comprise an additional rate term for taking into account the coding of the update parameter representative of the modification of the decoder. This additional rate term is represented by the term αΣ|w|0 in
On the decoder side, the update parameter m is used for updating (702) the DNN-based decoder. Optionally, the default reconstruction of the network can be used for closed loop predictive encoding (typically for video encoding), and the updated reconstruction for display. The default reconstruction of the network may correspond to the reconstructed output from the DNN-based decoder set with the parameters of the first training configuration.
The whole process is described here in the case of an update of the decoder for a second training configuration. However, the embodiments described herein are not limited to one additional training configuration. The DNN-decoder could be trained for one or more additional training configurations resulting in one or more update parameters for the DNN-decoder. Also, in any one of the embodiments described here, only the decoder part could be retrained in the second training configuration or both the encoder part and the decoder part of the auto-encoder can be jointly retrained in the second training configuration.
Below, some exemplary embodiments for modifying a decoder part of an auto-encoder are described. Similar adaptations of the network at the decoder are performed using the adaptation parameter sent to the decoder.
In the example shown in
According to an embodiment, the additional weights w are signaled in the coded video bit-stream or as side information. In a variant, other layers can be updated.
According to the embodiment illustrated in
According to another embodiment, the decoder features conditional layers, such as conditional convolutions. Such layers have two inputs: the tensor elements of the output of the previous layers and another tensor which defines the “condition”. The conditional tensor is usually a 2d or 3d tensor, encoded with a one-hot scheme. The tensor shape is 2d if the condition is applied globally, i.e. the condition is the same for all tensor elements or 3D if the condition is applied locally, i.e. the condition is specific for each tensor element.
In this case, the length of the last dimension K of the conditional tensors then depends on the number N of conditions or “modes”, with K=ceil(log 2(N)), ceil being a ceiling function.
In this variant, instead of adding layers or sending additional weights, integer values are signaled alongside the compressed latent to condition the decoding based on the desired output metric optimization. Each integer value is indexed based on the position of their respective conditional layers.
According to another variant, one-hot encoded vectors are sent alongside the compressed latent to condition the decoding. The conditional vectors are compressed and indexed based on the position of their conditional layers in the decoder.
For both variants, not all layers in the decoder need to be conditional.
According to this embodiment, the auto-encoder is jointly trained for all the conditions set for the decoding. For instance, according to the embodiment described with reference to
The exemplary modifications of the decoder part of the auto-encoder described above in relation with
According to this variant, the decoder outputs both the original reconstructed frame Îb corresponding to the output of the decoder when trained with a first training configuration, for instance with an objective metric and generic training set, and the frame Îs resulting from the training of the adapted layers.
In order to update the decoder, the update parameter is sent to the decoder in the form of one or more syntax elements. According to another variant, the update parameter can also be sent along with the bitstream comprising coded data representative of an image or a video. In this case, in order to decode the “subjective-quality” based reconstructed image or video, the additional syntax elements are sent to the decoder before decoding takes place.
Some variants of the syntax corresponding to the described decoder adaptation are presented in Table 1. The update parameter may comprise one or more of the syntax elements shown below.
The associated semantic is the following:
In a variant, standard methods and syntax to transmit NN models or model updates, such as MPEG7 NNR (compressed Neural Network Representations), can be used to convey the proposed model updates.
According to an example of the present principles, illustrated in
In accordance with an example, the network is a broadcast network, adapted to broadcast/transmit encoded update parameters or encoded images from device A to decoding devices including the device B.
A signal, intended to be transmitted by the device A, carries at least one bitstream comprising coded data representative of at least one update parameter for modifying a deep-neural-network-based decoder defined from a training of a deep neural network-based auto-encoder using a first training configuration. The bitstream may comprise syntax elements for the update parameter according to any one of the embodiments described above.
According to an embodiment, this signal may also carry on coded data representative of at one part of at least one image.
According to an embodiment, the payload comprises coded data representative of at least one part of at least one image encoded according to any one of the embodiments described above.
At training, the loss is adapted as follows: a regularization term is added to the loss to guarantee the added weights w sparsity. Here the parsimony is expressed using a L0 norm. A L1 norm can also be used. The parameter a allows to normalize the additional rate brought by the network update: for example, for a given image size to encode, the normalization factor takes into account the fact that the network update is sent only once for the whole image. For video, the network update is sent for example once every N images.
In this variant, instead of a weight sparsity, an entropy measure is used instead of a L0 norm. The entropy measure is more exactly a proxy of entropy as the one used in entropy bottleneck of compressive auto-encoder as in “Joint Autoregressive and hierarchical priors for learned image compression”, D. Minnen, J. Ballé, G. Toderici, NIPS 2018”. It guarantees that the weights update has a reasonable bitrate overhead. The loss is changed as: p(I, Î)+λ(R(b)+αH(w)), where H(x) is the estimated entropy of x.
During the training, both the encoder 1101 and the weights update w are changed. In this variant, the weights are increments from the default weights of the last layer. But, the weights could also be a new set of weights. In the loss function, the rate of the latent b for a set of samples and the rate of weights update b′ are used.
The weights update coding uses a fix, given entropy coder E and decoder E−1. These coder and decoder are fixed and known at the DNN-based decoder. As in the classical decoder, the weights are quantized. Other given coder/decoder can also be used to encode the update parameters, for example a given auto-encoder as in “Joint Autoregressive and hierarchical priors for learned image compression”, D. Minnen, J. Ballé, G. Toderici, NIPS 2018”, trained with a set of weights update. The weights update training set are for example given by domain adaptation or metric adaptation.
In order to fix the α coefficient, giving the balance between the rate of the latent and the rate of the weights update, several strategies are available depending on the application:
If only 1 image is to be sent, then α=1 because the weights update will be used only once together with the latent.
If many images are to be sent for a specific application, then α is decreased. If the number of images N to send is known, then α can be fixed to 1/N.
Various methods are described herein, and each of the methods comprises one or more steps or actions for achieving the described method. Unless a specific order of steps or actions is required for proper operation of the method, the order and/or use of specific steps and/or actions may be modified or combined. Additionally, terms such as “first”, “second”, etc. may be used in various embodiments to modify an element, component, step, operation, etc., for example, a “first decoding” and a “second decoding”. Use of such terms does not imply an ordering to the modified operations unless specifically required. So, in this example, the first decoding need not be performed before the second decoding, and may occur, for example, before, during, or in an overlapping time period with the second decoding.
Various methods and other aspects described in this application can be used to modify modules, of a video encoder 200 and decoder 300 as shown in
Various numeric values are used in the present application. The specific values are for example purposes and the aspects described are not limited to these specific values.
Various implementations involve decoding. “Decoding,” as used in this application, may encompass all or part of the processes performed, for example, on a received encoded sequence in order to produce a final output suitable for display. In various embodiments, such processes include one or more of the processes typically performed by a decoder, for example, entropy decoding, inverse quantization, inverse transformation, and differential decoding. Whether the phrase “decoding process” is intended to refer specifically to a subset of operations or generally to the broader decoding process will be clear based on the context of the specific descriptions and is believed to be well understood by those skilled in the art.
Various implementations involve encoding. In an analogous way to the above discussion about “decoding”, “encoding” as used in this application may encompass all or part of the processes performed, for example, on an input video sequence in order to produce an encoded bitstream.
The implementations and aspects described herein may be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed may also be implemented in other forms (for example, an apparatus or program). An apparatus may be implemented in, for example, appropriate hardware, software, and firmware. The methods may be implemented in, for example, an apparatus, for example, a processor, which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processors also include communication devices, for example, computers, cell phones, portable/personal digital assistants (“PDAs”), and other devices that facilitate communication of information between end-users.
Reference to “one embodiment” or “an embodiment” or “one implementation” or “an implementation”, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment” or “in one implementation” or “in an implementation”, as well any other variations, appearing in various places throughout this application are not necessarily all referring to the same embodiment.
Additionally, this application may refer to “determining” various pieces of information. Determining the information may include one or more of, for example, estimating the information, calculating the information, predicting the information, or retrieving the information from memory.
Further, this application may refer to “accessing” various pieces of information. Accessing the information may include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, moving the information, copying the information, calculating the information, determining the information, predicting the information, or estimating the information.
Additionally, this application may refer to “receiving” various pieces of information. Receiving is, as with “accessing”, intended to be a broad term. Receiving the information may include one or more of, for example, accessing the information, or retrieving the information (for example, from memory). Further, “receiving” is typically involved, in one way or another, during operations, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.
It is to be appreciated that the use of any of the following “/”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended, as is clear to one of ordinary skill in this and related arts, for as many items as are listed.
Also, as used herein, the word “signal” refers to, among other things, indicating something to a corresponding decoder. For example, in certain embodiments the encoder signals a quantization matrix for de-quantization. In this way, in an embodiment the same parameter is used at both the encoder side and the decoder side. Thus, for example, an encoder can transmit (explicit signaling) a particular parameter to the decoder so that the decoder can use the same particular parameter. Conversely, if the decoder already has the particular parameter as well as others, then signaling can be used without transmitting (implicit signaling) to simply allow the decoder to know and select the particular parameter. By avoiding transmission of any actual functions, a bit savings is realized in various embodiments. It is to be appreciated that signaling can be accomplished in a variety of ways. For example, one or more syntax elements, flags, and so forth are used to signal information to a corresponding decoder in various embodiments. While the preceding relates to the verb form of the word “signal”, the word “signal” can also be used herein as a noun.
As will be evident to one of ordinary skill in the art, implementations may produce a variety of signals formatted to carry information that may be, for example, stored or transmitted. The information may include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal may be formatted to carry the bitstream of a described embodiment. Such a signal may be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal. The formatting may include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information that the signal carries may be, for example, analog or digital information. The signal may be transmitted over a variety of different wired or wireless links, as is known. The signal may be stored on a processor-readable medium.
A number of embodiments have been described. Features of these embodiments can be provided alone or in any combination, across various claim categories and types. Further, embodiments can include one or more of the following features, devices, or aspects, alone or in any combination, across various claim categories and types:
| Number | Date | Country | Kind |
|---|---|---|---|
| 20305838.3 | Jul 2020 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2021/069291 | 7/12/2021 | WO |
