Patent Publication Number: US-11652994-B2

Title: Neural image compression with adaptive intra-prediction

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based on and claims priority to U.S. Provisional Patent Application No. 63/138,963, filed on Jan. 19, 2021, the disclosure of which is incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     Standard groups and companies have been actively searching for potential needs for standardization of future video coding technology. These standard groups and companies have established JPEG-AI groups focusing on AI-based end-to-end neural image compression using Neural Networks (NN). The success of recent approaches has brought more and more industrial interests in advanced neural image and video compression methodologies. 
     Given an input image x, the target of NIC uses the image x as the input to a NN encoder to compute a compressed representation  y  that is compact for storage and transmission, then use  y  as the input to a NN decoder to reconstruct an image  x . Previous NIC methods take a variational autoencoder (VAE) structure, where the NN encoders directly use the entire image x as its input, which is passed through a set of network layers that work like a black box to compute the output representation  y . Correspondingly, the NN decoders take the entire representation  y  as its input, which is passed through another set of network layers that work like another black box to compute the reconstructed  x . 
     The block-based intra-prediction and residual coding mechanism encodes residuals between prediction blocks and the original blocks instead of directly encoding the original whole image. This mechanism has been proven highly effective for compressing image frames in modern video coding standards like HEVC and VVC. Entire images are partitioned into blocks of various sizes, and a prediction block is generated by copying the boundary pixels of previous compressed blocks along a variety of angular directions, and then the residuals between the original block and the prediction block are compressed. Residuals can more efficiently be encoded compared to the original pixels and, therefore, better coding performance can be achieved. Different block sizes have direct impact on the compression performance, and the optimal block size usually depends on specific images. 
     SUMMARY 
     According to embodiments, a method of neural image compression with adaptive intra-prediction is performed by at least one processor and includes receiving an optimal partition, receiving a compressed representation of an input comprising a first set of blocks, for each block in the first set of blocks, receiving a block selection signal indicating one of a first recovered block and a second recovered block as a currently recovered block, and based on the received block selection signal, performing one of a first recovery and a second recovery, and merging the currently recovered blocks to obtain a reconstructed image. The first recovery comprises: compressing the block in the first set of blocks, using a first neural network, to compute a first compressed representation, and decompressing the first compressed representation, using a second neural network, to compute the first recovered block. The second recovery comprises: computing a first predicted block based on a set of previously recovered blocks and a set of previously recovered micro-blocks, computing a first residual based on a current block in the first set of blocks and the predicted block, generating a recovered residual based on the first residual, and partitioning the first predicted block and adding the recovered residual to obtain the second recovered block. 
     According to embodiments, an apparatus for neural image compression with adaptive intra-prediction includes at least one memory configured to store program code, and at least one processor configured to read the program code and operate as instructed by the program code. The program code including first receiving code configured to cause the at least one processor to receive an optimal partition, second receiving code configured to cause the at least one processor to receive a compressed representation of an input comprising a first set of blocks, third receiving code configured to cause the at least one processor to, for each block in the first set of blocks, receive a block selection signal indicating one of a first recovered block and a second recovered block as a currently recovered block, and execute one of a first recovery code and a second recovery code, and merging code configured to cause the at least one processor to merge each of the currently recovered blocks to obtain a reconstructed image. Further, wherein the first recovery comprises: first compressing code configured to cause the at least one processor to compress the block in the first set of blocks, using a first neural network, to compute a first compressed representation, and first decompressing code configured to cause the at least one processor to decompress the first compressed representation, using a second neural network, to compute the first recovered block, and wherein the second recovery comprises: first predicting code configured to cause the at least one processor to predict a first predicted block based on a set of previously recovered blocks and a set of previously recovered micro-blocks, first residual code configured to cause the at least one processor to compute a first residual based on a current block in the first set of blocks and the predicted block, first generating code configured to cause the at least one processor to generate a recovered residual based on the first residual, and first partitioning code configured to cause the at least one processor to partition the first predicted block and adding the recovered residual to obtain the second recovered block. 
     According to embodiments, a non-transitory computer-readable medium storing instructions that, when executed by at least one processor for neural image compression with adaptive intra-prediction, cause the at least one processor to receive an optimal partition, receive a compressed representation of an input comprising a first set of blocks, and for each block in the first set of blocks, receive a block selection signal indicating one of a first recovered block and a second recovered block as a currently recovered block, and execute one of a first recovery and a second recovery, and merge each of the currently recovered blocks to obtain a reconstructed image, wherein the first recovery comprises: compress the block in the first set of blocks, using a first neural network, to compute a first compressed representation, and decompress the first compressed representation, using a second neural network, to compute the first recovered block, and wherein the second recovery comprises: predict a first predicted block based on a set of previously recovered blocks and a set of previously recovered micro-blocks, compute a first residual based on a current block in the first set of blocks and the predicted block, generate a recovered residual based on the first residual, and partition the first predicted block and adding the recovered residual to obtain the second recovered block. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagram of an environment in which methods, apparatuses and systems described herein may be implemented, according to embodiments. 
         FIG.  2    is a block diagram of example components of one or more devices of  FIG.  1   . 
         FIG.  3    is a block diagram of an NIC Encoder apparatus, during a test stage, according to embodiments. 
         FIG.  4    is a detailed workflow of the Partition Selection module of  FIG.  3   , during a test stage, according to embodiments. 
         FIG.  5    is a block diagram of an NIC Decoder apparatus, during a test stage, according to embodiments. 
         FIG.  6    is a workflow of a NIC intra-prediction apparatus, during a training stage, according to embodiments. 
         FIG.  7    is a flowchart of a method of neural image compression with adaptive intra-prediction, according to the embodiments. 
         FIG.  8    is a block diagram of an apparatus for neural image compression with adaptive intra-prediction, according to the embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure proposes a Neural Image Compression (NIC) framework of compressing an input image by a Neural Network (DNN) using a block-based intra-prediction mechanism with adaptive block sizes. Example embodiments will be described below with reference to the drawings. In the drawings, the same modules are denoted by the same reference numbers, and thus a repeated description may be omitted as needed.  FIG.  1    is a diagram of an environment  100  in which methods, apparatuses and systems described herein may be implemented, according to embodiments. 
     As shown in  FIG.  1   , the environment  100  may include a user device  110 , a platform  120 , and a network  130 . Devices of the environment  100  may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections. 
     The user device  110  includes one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with platform  120 . For example, the user device  110  may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smart phone, a radiotelephone, etc.), a wearable device (e.g., a pair of smart glasses or a smart watch), or a similar device. In some implementations, the user device  110  may receive information from and/or transmit information to the platform  120 . 
     The platform  120  includes one or more devices as described elsewhere herein. In some implementations, the platform  120  may include a cloud server or a group of cloud servers. In some implementations, the platform  120  may be designed to be modular such that software components may be swapped in or out. As such, the platform  120  may be easily and/or quickly reconfigured for different uses. 
     In some implementations, as shown, the platform  120  may be hosted in a cloud computing environment  122 . Notably, while implementations described herein describe the platform  120  as being hosted in the cloud computing environment  122 , in some implementations, the platform  120  may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based. 
     The cloud computing environment  122  includes an environment that hosts the platform  120 . The cloud computing environment  122  may provide computation, software, data access, storage, etc. services that do not require end-user (e.g., the user device  110 ) knowledge of a physical location and configuration of system(s) and/or device(s) that hosts the platform  120 . As shown, the cloud computing environment  122  may include a group of computing resources  124  (referred to collectively as “computing resources  124 ” and individually as “computing resource  124 ”). 
     The computing resource  124  includes one or more personal computers, workstation computers, server devices, or other types of computation and/or communication devices. In some implementations, the computing resource  124  may host the platform  120 . The cloud resources may include compute instances executing in the computing resource  124 , storage devices provided in the computing resource  124 , data transfer devices provided by the computing resource  124 , etc. In some implementations, the computing resource  124  may communicate with other computing resources  124  via wired connections, wireless connections, or a combination of wired and wireless connections. 
     As further shown in  FIG.  1   , the computing resource  124  includes a group of cloud resources, such as one or more applications (“APPs”)  124 - 1 , one or more virtual machines (“VMs”)  124 - 2 , virtualized storage (“VSs”)  124 - 3 , one or more hypervisors (“HYPs”)  124 - 4 , or the like. 
     The application  124 - 1  includes one or more software applications that may be provided to or accessed by the user device  110  and/or the platform  120 . The application  124 - 1  may eliminate a need to install and execute the software applications on the user device  110 . For example, the application  124 - 1  may include software associated with the platform  120  and/or any other software capable of being provided via the cloud computing environment  122 . In some implementations, one application  124 - 1  may send/receive information to/from one or more other applications  124 - 1 , via the virtual machine  124 - 2 . 
     The virtual machine  124 - 2  includes a software implementation of a machine (e.g., a computer) that executes programs like a physical machine. The virtual machine  124 - 2  may be either a system virtual machine or a process virtual machine, depending upon use and degree of correspondence to any real machine by the virtual machine  124 - 2 . A system virtual machine may provide a complete system platform that supports execution of a complete operating system (“OS”). A process virtual machine may execute a single program, and may support a single process. In some implementations, the virtual machine  124 - 2  may execute on behalf of a user (e.g., the user device  110 ), and may manage infrastructure of the cloud computing environment  122 , such as data management, synchronization, or long-duration data transfers. 
     The virtualized storage  124 - 3  includes one or more storage systems and/or one or more devices that use virtualization techniques within the storage systems or devices of the computing resource  124 . In some implementations, within the context of a storage system, types of virtualizations may include block virtualization and file virtualization. Block virtualization may refer to abstraction (or separation) of logical storage from physical storage so that the storage system may be accessed without regard to physical storage or heterogeneous structure. The separation may permit administrators of the storage system flexibility in how the administrators manage storage for end users. File virtualization may eliminate dependencies between data accessed at a file level and a location where files are physically stored. This may enable optimization of storage use, server consolidation, and/or performance of non-disruptive file migrations. 
     The hypervisor  124 - 4  may provide hardware virtualization techniques that allow multiple operating systems (e.g., “guest operating systems”) to execute concurrently on a host computer, such as the computing resource  124 . The hypervisor  124 - 4  may present a virtual operating platform to the guest operating systems, and may manage the execution of the guest operating systems. Multiple instances of a variety of operating systems may share virtualized hardware resources. 
     The network  130  includes one or more wired and/or wireless networks. For example, the network  130  may include a cellular network (e.g., a fifth generation (5G) network, a long-term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, or the like, and/or a combination of these or other types of networks. 
     The number and arrangement of devices and networks shown in  FIG.  1    are provided as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown in  FIG.  1   . Furthermore, two or more devices shown in  FIG.  1    may be implemented within a single device, or a single device shown in  FIG.  1    may be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of the environment  100  may perform one or more functions described as being performed by another set of devices of the environment  100 . 
       FIG.  2    is a block diagram of example components of one or more devices of  FIG.  1   . 
     A device  200  may correspond to the user device  110  and/or the platform  120 . As shown in  FIG.  2   , the device  200  may include a bus  210 , a processor  220 , a memory  230 , a storage component  240 , an input component  250 , an output component  260 , and a communication interface  270 . 
     The bus  210  includes a component that permits communication among the components of the device  200 . The processor  220  is implemented in hardware, firmware, or a combination of hardware and software. The processor  220  is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, the processor  220  includes one or more processors capable of being programmed to perform a function. The memory  230  includes a random access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and/or an optical memory) that stores information and/or instructions for use by the processor  220 . 
     The storage component  240  stores information and/or software related to the operation and use of the device  200 . For example, the storage component  240  may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, and/or a solid state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of non-transitory computer-readable medium, along with a corresponding drive. 
     The input component  250  includes a component that permits the device  200  to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and/or a microphone). Additionally, or alternatively, the input component  250  may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, and/or an actuator). The output component  260  includes a component that provides output information from the device  200  (e.g., a display, a speaker, and/or one or more light-emitting diodes (LEDs)). 
     The communication interface  270  includes a transceiver-like component (e.g., a transceiver and/or a separate receiver and transmitter) that enables the device  200  to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface  270  may permit the device  200  to receive information from another device and/or provide information to another device. For example, the communication interface  270  may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like. 
     The device  200  may perform one or more processes described herein. The device  200  may perform these processes in response to the processor  220  executing software instructions stored by a non-transitory computer-readable medium, such as the memory  230  and/or the storage component  240 . A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space spread across multiple physical storage devices. 
     Software instructions may be read into the memory  230  and/or the storage component  240  from another computer-readable medium or from another device via the communication interface  270 . When executed, software instructions stored in the memory  230  and/or the storage component  240  may cause the processor  220  to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software. 
     The number and arrangement of components shown in  FIG.  2    are provided as an example. In practice, the device  200  may include additional components, fewer components, different components, or differently arranged components than those shown in  FIG.  2   . Additionally, or alternatively, a set of components (e.g., one or more components) of the device  200  may perform one or more functions described as being performed by another set of components of the device  200 . 
     Methods and apparatuses for NIC using block-based intra-prediction with adaptive block sizes will now be described in detail. 
     This disclosure proposes an NIC framework using block-based intra-prediction with adaptive block sizes. Residuals between prediction blocks and the original blocks are encoded instead of encoding the original pixels, and the block size is adaptively determined based on the compression quality such as the Rate-Distortion (R-D) loss. 
       FIG.  3    is a block diagram of an NIC encoder  300  apparatus, during a test stage, according to embodiments. 
     As shown in  FIG.  3   , the encoder  300  includes a Partition module  310  and a Partition Selection module  320 . 
     On the encoder side, given the input image x, the Partition module  310  partitions the input image x into k micro-blocks of size (w m , h m ), M 1   k ={m 1 , . . . , m k }, where m i  denotes the i-th micro-block. Each micro-block m i  may be further partitioned into blocks b i,1 , . . . , b i,n , where b i,j  is the j-th block in the micro-block m i . The size of the block b i,j  can vary for different blocks. In an example embodiment, the micro-blocks align with the CTU partition in current video coding tools. Each CTU micro-block may be further partitioned into 2×2, 4×4, 8×8, 16×16, 32×32, or 64×64 blocks. Embodiments do not put any restrictions on the size of the CTU or how blocks in the CTU are partitioned. 
     Assume that there are P different ways to partition each micro-block m i  into blocks. The workflow for how to determine the optimal way for partitioning in the Partition Selection module  320  will now be described in detail. 
       FIG.  4    is a detailed workflow of the Partition Selection module  320  of  FIG.  3   , during a test stage, according to embodiments. 
     As shown in  FIG.  4   , the Partition Selection module  320  includes an Intra-Prediction module  410 , a Residual Neural Compression module  420 , a Residual Neural Decompression module  430 , a Compute Residual Compression Loss module  440 , a Neural Compression module  450 , a Neural Decompression module  460 , a Compute Compression Loss module  470 , a Block Selection module  480 , and a Compute Partition Loss module  490 . 
     The output of the Partition Selection module  320  include the optimal way of partition p*, a set of block selection signals S i,p     *       1     k ={s i,p     *     ,1 , . . . s i,p     *     ,q }, and a set of compressed representations {tilde over (B)} i,p*     1     q ={{tilde over (b)} i,p*,1 , . . . , {tilde over (b)} i,p*,q }. These outputs will then be sent to the decoder side (for example, decoder  500  detailed in  FIG.  5   ), typically after being further compressed by quantization and entropy coding. 
     Let B i,p     1     n     p   ={b i,p,1 , . . . , b i,p,n     p   } denote blocks obtained by the p-th way of partition. The total number of blocks n p  for this partition is automatically determined by the size of the micro-block m i  and the size of the block. For each partitioned block b i,p,j , a predicted block  b   i,p,j  may be computed by the Intra-Prediction module  410  based on a Prediction Network. The Prediction Network takes as input a set of image pixels selected from x, where the selected pixels can come from two sources: from the micro-blocks M 1   i−1 ={m 1 , . . . , m i−1 } that are encoded before the micro-block m i , and from the blocks B i,p     1     j−1 ={b i,p,1 , . . . , b i,p,j− } in the micro-block m i  that are encoded before the partitioned block b i,p,j . There are many ways to select the pixels and form the input of the Prediction Network. For example, the neighboring pixels that are spatially closest to the partitioned block b i,p,j  in a context area may be organized in some order (stacking, concatenation, spatially transformed etc.) to form the input to the Prediction Network. Through inference computation, the Prediction Network outputs the predicted block  b   i,p,j . The Prediction Network can have various architectures. For each way of partition, the Prediction Network can use a different NN model for its prediction. Typically, convolutional and fully connected layers will be used. Embodiments do not put any restrictions on the size and shape of the context area for pixel selection, the way pixels are transformed into the input of the Prediction Network, or the network architectures of the Prediction Network. 
     For the partitioned block b i,p,j , after computing the predicted block  b   i,p,j , a residual r i,p,j  may be computed based on the partitioned block b i,p,j  and the predicted block  b   i,p,j , e.g. by subtraction. Let R i,p     1     n     p   ={r i,p,1 , . . . , r i,p,n     p   } denote the residual of the entire i-th micro-block m i  partitioned in the p-th way. This residual R i,p     1     n     p    may be re-partitioned into a set of q residual blocks R i,p     1     q ={r i,p,1   r , . . . , r i,p,q   r }. Note that the re-partitioning of the residual block can be the same or different from the original partitioning of the predicted blocks  b   i,p,j . When q=1, the entire micro-block will be processed as one piece. The corresponding micro-block B i,p     1     n     p   ={b i,p,1 , . . . , b i,p,n     p   } and predicted blocks  B   i,p     1     n     p   ={ b   i,p,1 , . . . ,  b   i,p,n     p   } can also be re-partitioned in the same way into a re-partitioned micro-block B i,p     1     rq ={b i,p,1   r , . . . , b i,p,q   r } and a re-partitioned predicted block  B   i,p     1     rq ={ b   i,p,1   r , . . . ,  b   i,p,q   r }, respectively. The Residual Neural Compression module  420  may compress each residual block r i,p,j   r  to compute a compressed residual representation {tilde over (r)} i,p,j   r , which is decompressed by the Residual Neural Decompression module  430  to compute a recovered residual block {circumflex over (r)} i,p,j   r . The recovered residual block {circumflex over (r)} i,p,j  can be added back to corresponding re-partitioned predicted block  b   i,p,j   r  to obtain a reconstructed block {circumflex over (b)} i,p,j   r . The Compute Residual Compression Loss module  440  computes a residual compression quality loss L i,p,j   r  based on the re-partition micro-block b i,p,j   r , the reconstructed block {circumflex over (b)} i,p,j   r  and the compressed residual representation {tilde over (r)} i,p,j   r . For example, in an example embodiment, the Rate-Distortion (R-D) loss may be computed as the quality measurement (residual quality loss), as follows: 
     
       
         
           
             
               
                 
                   
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     Where D(b i,p,j   r ,{circumflex over (b)} i,p,j   r ) is the distortion between the re-partition micro-block b i,p,j   r  and the reconstructed block {circumflex over (b)} i,p,j   r . R({tilde over (r)} i,p,j   r ) is the rate loss measuring the bit consumption of the compressed residual representation {tilde over (r)} i,p,j   r . λ is a trade-off hyperparameter balancing the importance of different terms. Other compression quality loss can certainly be used here. Embodiments do not put any restrictions on the specific measurement used for the compression quality loss, the distortion, or the rate loss. 
     At the same time, each original block b i,p,j   r  may be directly compressed by the Neural Compression module  450  to compute a compressed representation {tilde over (b)} i,p,j   r , which is decompressed by the Neural Decompression module  460  to compute a recovered block {circumflex over (b)} i,p,j   b  directly. A compression quality loss L i,p,j   b  may be computed in the Compute Compression Loss module  470  based on the original block b i,p,j   r , the reconstructed block {circumflex over (b)} i,p,j   b , and the compressed representation {tilde over (b)} i,p,j   r  in the same way as the residual quality loss L i,p,j   r . Based on the compression quality loss L i,p,j   b  and the residual quality loss L i,p,j   r , the Block Selection module  480  generates a selection signal s i,p,j  to indicate whether the residual block r i,p,j   r  or the original b i,p,j   r  will be used to generate the compressed residual representation {tilde over (r)} i,p,j   r  or the compressed representation {tilde over (b)} i,p,j   r , e.g., by selecting the option with less quality loss. This gives the optimal quality loss L* i,p,j  for compressing the current j-th block b i,p,j   r , e.g., L* i,p,j =min(L i,p,j   b ,L i,p,j   r ). The Compute Partition Loss module  490  computes the overall quality loss L i,p  for the p-th way of partition of micro-block m i  as: 
     
       
         
           
             
               
                 
                   
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     Where each w i,p,j  is a weight associated with the original block b i,p,j   r . By simply setting all weights to be 1, all blocks are treated equally. Some blocks may be treated with more attention than others, and an attention map (or significance map) can be used to obtain the weights. 
     By repeating the same process for all P ways of partition, the quality loss L i,p ,p=1, . . . , P may be obtained. The optimal way of partition p* can then be selected, e.g., as the partition with the optimal loss (i.e. p*=argmin p L i,p ,L* i =min p L i,p ). The corresponding block selection signals S i,p*     1     k ={s i,p*,1 , . . . , s i,p*,q } may also be determined as output of the Partition Selection module  320 . 
     Let B i,p*     1     n     p*   ={b i,p*,1 , . . . , b i,p*,n     p*   } denote the selected optimally partitioned blocks for micro-block m i . According to the block selection signal s i,p*,j , the corresponding compressed residual representation {tilde over (r)} i,p*,j   r  or the compressed representation {tilde over (b)} i,p*,j   r  can also be determined to be the actual compressed representation {tilde over (b)} i,p     *     ,j   r  for block b i,p     *     ,j   r . The set of compressed representations {tilde over (B)} i,p*     1     q ={{tilde over (b)} i,p*,1 , . . . , {tilde over (b)} i,p*,q } are also output from the Partition Selection module  320 . The optimal partition p*, the compressed representation {tilde over (B)} i,p*     1     q , and the block selection signals S i,p*     1     q  are further encoded, e.g., through quantization and entropy encoding, to generate the encoded stream and sent to the decoder side (detailed in  FIG.  5   ). 
     The Neural Compression module  450  and the Residual Neural Compression module  420  can use any neural compression methods. Embodiments do not put any restrictions on the specific methods or network architectures used for these two modules. 
       FIG.  5    is a block diagram of an NIC decoder  500  apparatus, during a test stage, according to embodiments. 
     As shown in  FIG.  5   , the decoder  500  includes the Intra-Prediction module  410 , the Residual Neural Decompression module  430 , the Neural Decompression module  460 , and a Merging module  510 . 
     On the decoder  500  side, the system receives the optimal partition p*, the compressed representation {tilde over (B)} i,p*     1     q , and the block selection signals S i,p*     1     q ={s i,p*,1 , . . . s i,p*,q } (typically recovered from the received bitstream by entropy decoding and dequantization). Based on each block selection signal s i,p*,j , the system selects one of the following methods to compute the recovered block {circumflex over (b)} i,p*,j . If the selection signal s i,p*,j  indicates that the recovered block comes from the decoded block based on the compressed representation {tilde over (b)} i,p,j   r  on the encoder  300  side, the Neural Decompression module  460  will be used to compute the recovered block {tilde over (b)} i,p*,j   b . If the selection signal s i,p*,j  indicates that the recovered block comes from the decoded block based on the compressed residual representation {tilde over (r)} i,p,j   r  on the encoder  300  side, the Residual Neural Decompression module  430  will be used to compute the recovered residual {tilde over (r)} i,p*,j   r . In the case where the recovered residual {circumflex over (r)} i,p*,j   r  is used, based on the set of previously recovered blocks {circumflex over (B)} i,p*     1     i−1 ={{circumflex over (b)} i,p*,1 , . . . , {circumflex over (b)} i,p*,j−1 } and previously recovered micro-blocks {circumflex over (m)} 1 , . . . , {circumflex over (m)} i−1 , the Intra-Prediction module  410  computes the predicted block  b   i,p*,j  by using the Prediction Network the same way it is computed in the encoder  300 . The only difference being that, on the encoder  300  side, the input of the Prediction Network is formed by pixels of the original input image x. On the decoder  500  side, the inputs are from the corresponding recovered blocks and micro-blocks. The recovered residual {circumflex over (r)} i,p*,j   r  can then be added back to the re-partitioned (in the same way as the encoder  300 ) predicted block  b   i,p*,j   r  to obtain the recovered block {circumflex over (b)} i,p*,j   r . Either of the computed recovered block ({circumflex over (b)} i,p*,j  or {circumflex over (b)} i,p*,j   b ) will give the actual recovered block {circumflex over (b)} i,p*,j , and the decoder moves on to process the next block. Finally, the recovered blocks {circumflex over (B)} i,p*     1     n     p*   ={{circumflex over (b)} i,p*,1 , . . . , {circumflex over (b)} i,p*,n     p*   } of micro-block m i  will be aggregated into the reconstructed image  x  in the Merging module  510 . In some embodiment, the Merging module  510  can further process the recovered blocks to remove the artifacts, such as deblocking, denoising, etc. Embodiments do not put any restrictions on the specific methods for how the recovered blocks are aggregated into the reconstructed image  x . 
     The NIC intra-prediction training process will now be described.  FIG.  6    is a workflow of a NIC intra-prediction apparatus  600 , during a training stage, according to embodiments. 
     As shown in  FIG.  6   , the NIC intra-prediction training apparatus  600  includes the Partition module  310 , the Partition Selection module  320 , the Intra-Prediction module  410 , the Residual Neural Decompression module  430 , the Neural Decompression module  460 , the Merging module  510 , a Compute Overall Loss module  610 , and a Compute Additional Loss module  620 . 
     The target of the training process is to learn the Prediction Network, the Neural Compression module  450 , the Neural Decompression module  460 , the Residual Neural Compression module  420 , and the Residual Neural Decompression module  430 . In the case where the learnable Merging module  510  and Block Selection module  480  are used, e.g., when an NN is used for aggregating recovered blocks into the recovered image, the corresponding learnable parameters can also be learned in the training process. In the training process, the weight coefficients of the above networks and modules to be learned are initialized, for example, by using pre-trained models, or by setting their parameters to random numbers. Then, given an input training image x, it is passed through the encoder  300  described in  FIG.  3   , followed by the decoder  500  described in  FIG.  5   , to compute the optimal partition p*, the compressed representation {tilde over (B)} i,p*     1     q ={{tilde over (b)} i,p*,1 , . . . , {tilde over (b)} i,p*,q }, the block selection signals S i,p*     1     k ={s i,p*,1 , . . . , s i,p*d,q } for each micro-block m i , and the final reconstructed image  x . A distortion loss D(x,  x ) may be computed, such as the traditional PSNR, MS-SSIM, or a weighted combination of both. A rate loss R({tilde over (B)} i,p*     1     q ) may be computed to measure the bit consumption of the compressed representation {tilde over (B)} i,p*     1     q . Therefore, an overall R-D loss L(x,  x , {tilde over (B)} 1,p*     1     q , . . . , {tilde over (B)} k,p*     1     q ) can be computed in the Compute Overall Loss module  610 : 
     
       
         
           
             
               
                 
                   
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     Where α, β i  are hyperparameters balancing the importance of different terms. 
     Other forms of loss, such as the distortion loss D(r i,p*,j   r , {circumflex over (r)} i,p*,j   r ) between the recovered residual {circumflex over (r)} i,p*,j   r  and the original residual r i,p*,j   r , and the distortion loss D(b i,p*,j   r , {circumflex over (b)} i,p*,j   b ) may also be computed in the Compute Additional Loss module  620 , e.g., the MSE or SSIM measurements. D(r i,p*,j   r ,{circumflex over (r)} i,p*,j   r ) and D(b i,p*,j   r ,{circumflex over (b)} i,p*,j   b ) can also be optionally combined with the overall R-D loss L(x,  x , {tilde over (B)} 1,p*     1     q , . . . , {tilde over (B)} k,p*     1     q ) into a final loss of the entire system. The gradient of the final loss can be computed and back-propagated to update the learnable parameters in the system. Note that, different components (i.e., networks or modules) can be updated at different times with different updating frequencies. In some embodiments, some components or part of the parameters in some components can be pre-trained and fixed, and the training process only updates the remaining parameters. 
       FIG.  7    is a flowchart of a method of neural image compression with adaptive intra-prediction, according to the embodiments. 
     In some implementations, one or more process blocks of  FIG.  7    may be performed by the platform  120 . In some implementations, one or more process blocks of  FIG.  7    may be performed by another device or a group of devices separate from or including the platform  120 , such as the user device  110 . Although  FIG.  7    shows example blocks of the method, in some implementations, the method may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in  FIG.  7   . Additionally, or alternatively, two or more of the blocks of the method may be performed in parallel. 
     As shown in  FIG.  7   , in operation  701 , the method includes receiving an optimal way of partition and receiving a compressed representation of an input comprising a first set of blocks. Operations  702 - 709  are performed for each block in the first set of blocks. 
     In operation  702 , the method of  FIG.  7    includes receiving a block selection signal indicating one of a first recovered block and a second recovered block as a currently recovered block. 
     In operation  703 , based on the selection signal the method continues to one of operations  704 - 705  or operations  706 - 709 . 
     In operation  704 , the method of  FIG.  7    includes compressing the block in the first set of blocks, using a first neural network, to compute a first compressed representation. 
     In operation  705 , the method of  FIG.  7    includes decompressing the first compressed representation, using a second neural network, to compute the first recovered block. 
     In operation  706 , the method of  FIG.  7    includes computing a first predicted block based on a set of previously recovered blocks and a set of previously recovered micro-blocks. 
     In operation  707 , the method of  FIG.  7    includes computing a first residual based on a current block in the first set of blocks and the predicted block. 
     In operation  708 , the method of  FIG.  7    includes generating a recovered residual based on the first residual. 
     In operation  709 , the method of  FIG.  7    includes partitioning the first predicted block and adding the recovered residual to obtain the second recovered block. 
     In operation  710 , the method of  FIG.  7    includes merging each of the currently recovered blocks to obtain a reconstructed image. 
       FIG.  8    is a block diagram of an apparatus for neural image compression with adaptive intra-prediction, according to the embodiments. 
     As shown in  FIG.  8   , the apparatus includes first receiving code  801 , second receiving code  802 , third receiving code  803 , first compressing code  804 , first decompressing code  805 , first predicting code  806 , first residual code  807 , first generating code  808 , first partitioning code  809 , and merging code  810 . 
     The first receiving code  801  is configured to cause at least one processor to receive an optimal way of partition. 
     The second receiving code  802  configured to cause the at least one processor to receive a compressed representation of an input comprising a first set of blocks, and for each block in the first set of blocks. 
     The third receiving code  803  configured to cause the at least one processor to receive a block selection signal indicating one of a first recovered block and a second recovered block as a currently recovered block. 
     The first compressing code  804  configured to cause the at least one processor to compress the block in the first set of blocks, using a first neural network, to compute a first compressed representation. 
     The first decompressing code  805  configured to cause the at least one processor to decompress the first compressed representation, using a second neural network, to compute the first recovered block. 
     The first predicting code  806  configured to cause the at least one processor to predict a first predicted block based on a set of previously recovered blocks and a set of previously recovered micro-blocks. 
     The first residual code  807  configured to cause the at least one processor to compute a first residual based on a current block in the first set of blocks and the predicted block. 
     The first generating code  808  configured to cause the at least one processor to generate a recovered residual based on the first residual. 
     The first partitioning code  809  configured to cause the at least one processor to partition the first predicted block and adding the recovered residual to obtain the second recovered block. 
     The merging code  810  configured to cause the at least one processor to merge each of the currently recovered blocks to obtain a reconstructed image. 
     Although  FIG.  8    shows example blocks of the apparatus, in some implementations, the apparatus may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in  FIG.  8   . Additionally, or alternatively, two or more of the blocks of the apparatus may be combined. 
     The embodiments describe the idea of adaptive block partition and block compression method selection using intra-prediction with the original image pixels, and the idea of using different block sizes for intra-prediction residual generation and block-wise neural compression. This method of NIC encoding and decoding advantageously results in a flexible and general framework that accommodates different intra-prediction methods, different neural compression methods for both residuals and original image blocks, different micro-block and block partitions. 
     The proposed NIC coding methods may be used separately or combined in any order. Further, each of the methods (or embodiments), encoder, and decoder may be implemented by processing circuitry (e.g., one or more processors or one or more integrated circuits). In one example, the one or more processors execute a program that is stored in a non-transitory computer-readable medium. 
     The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations. 
     As used herein, the term component is intended to be broadly construed as hardware, firmware, or a combination of hardware and software. 
     It will be apparent that systems and/or methods, described herein, may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and/or methods were described herein without reference to specific software code—it being understood that software and hardware may be designed to implement the systems and/or methods based on the description herein. 
     Even though combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set. 
     No element, act, or instruction used herein may be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, etc.), and may be used interchangeably with “one or more.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.