Abstract: The present disclosure relates to a computer-implemented method for processing video data. The method comprises receiving a user input corresponding to a first picture of the video data, generating, based on the user input, prediction information of the first picture with respect a reference picture of the video data, and encoding the first picture using the prediction information.

Abstract: The present disclosure relates to a system and method for machine learning from partial compressed representation. In some embodiments, an exemplary machine learning system includes: a compressor having circuitry configured to use a compression neural network to compress an image into a compressed representation, the compressed representation comprising a sequence of compressed channels; a selector having circuitry configured to select a part of the compressed channels from the compressed representation; and a learning module having circuitry configured to perform a learning task on the selected compressed channels.

Abstract: A method and apparatus for characteristic-based video processing include: in response to receiving a region of a picture of a video sequence, determining a characteristic in the region, the region being independent of other regions of the picture for video coding; determining a class associated with the region based on the characteristic, the class being selected from a plurality of classes; and encoding the region using a parameter set associated with the class, the parameter set being selected from a plurality of parameter sets for video coding at different quality levels.

Abstract: Systems and methods for dynamically encoding and decoding binary numbers using linear-time algorithms that encode and decode Hamming Distance-Based representations for the binary numbers are described. The binary numbers may correspond with integer values, such as 64-bit, 128-bit, or 256-bit integer values. In some cases, in response to detecting that a binary number is to be stored using a particular type of memory (e.g., a phase change memory), the binary number may first be encoded using a Hamming Distance-Based representation and then the encoded data may be written to the particular type of memory. The binary number may be encoded by generating a binary string or a binary array representing the binary number such that if one bit flips within the binary string or the binary array, the maximum distortion in the number is less than a threshold amount (e.g., less than 256).

Abstract: A system for using decoder information in video super resolution processing. A compressed video buffering module is used for receiving a compressed video stream and a decoder module is used for decoding the compressed video stream into an uncompressed stream and extracting motion vector information from the uncompressed stream. A video super resolution deep neural network processor module is used for processing the uncompressed stream in conjunction with the motion vector information to produce a video super resolution stream. An output buffer module is used for buffering the video super resolution stream for subsequent output.

Abstract: Systems and methods for implementing data protection techniques with symbol-based variable node updates for binary low-density parity-check (LDPC) codes are described. A semiconductor memory (e.g., a NAND flash memory) may read a set of data from a set of memory cells, determine a set of data state probabilities for the set of data based on sensed threshold voltages for the set of memory cells, generate a valid codeword for the set of data using an iterative LDPC decoding with symbol-based variable node updates and the set of data state probabilities, and store the valid codeword within the semiconductor memory or transfer the valid codeword from the semiconductor memory. The iterative LDPC decoding may utilize a message passing algorithm in which outgoing messages from a plurality of multi-variable nodes are generated using incoming messages (e.g., log-likelihood ratios or L-values) from a plurality of check nodes.

Abstract: Video coding techniques including variable bitrate encoding based on regions-of-interest (ROIs) and the type of the video content, the type of sets of frames of the video content, the type of scenes of the video content, or the like.

Abstract: A video processing apparatus includes a programmable hardware encoder configured to execute an encoding process on a plurality of input video frames. The video processing apparatus further includes a controller coupled with the programmable hardware encoder. The controller is configured to execute a set of instructions to cause the video processing apparatus to: determine first information of the plurality of input video frames, and adjust the encoding process based on the first information.

Abstract: A neural network, trained on a plurality of random size data samples, can receive a plurality of inference data samples including samples of different sizes. The neural network can generate feature maps of the plurality of inference data samples. Pooling can be utilized to generate feature maps having a fixed size. The fixed size feature maps can be utilized to generate an indication of a class for each of the plurality of inference data samples.

Abstract: The present disclosure relates to a method and an apparatus for map reduce. In some embodiments, an exemplary processing unit includes: a 2-dimensional (2D) processing element (PE) array comprising a plurality of PEs, each PE comprising a first input and a second input, the first inputs of the PEs in a linear array in a first dimension of the PE array being connected in series and the second inputs of the PEs in a linear array in a second dimension of the PE array being connected in parallel, each PE being configured to perform an operation on data from the first input or second input; and a plurality of reduce tree units, each reduce tree unit being coupled with the PEs in a linear array in the first dimension or the second dimension of the PE array and configured to perform a first reduction operation.

Abstract: Embodiments of the disclosure provide systems and methods for processing video content. The method can include: receiving raw video data of a video; determining a texture complexity for the video based on the raw video data; determining an encoding mode for the raw video data based on the texture complexity; and encoding the raw video data using the determined encoding mode.

Abstract: Methods and apparatuses for video transcoding based on spatial or temporal importance include: in response to receiving an encoded video bitstream, decoding a picture from the encoded video bitstream; determining a first level of spatial importance for a first region of a background of the picture based on an image segmentation technique; applying to the first region a first resolution-enhancement technique associated with the first level of spatial importance for increasing resolution of the first region by a scaling factor, wherein the first resolution-enhancement technique is selected from a set of resolution-enhancement techniques having different computational complexity levels; and encoding the first region using a video coding standard.

Abstract: A system for determining the importance of encoded image components for artificial intelligence tasks includes an image capture or storage unit, a processor and a communication interface. The processor can receive components of transformed domain image data from the one or more image capture or storage units across the communication interface. The processor can be configured to determine the relative importance of the components of the transformed domain image data for an artificial intelligence task.

Abstract: An innovative low-bit-width device may include a first digital-to-analog converter (DAC), a second DAC, a plurality of non-volatile memory (NVM) weight arrays, one or more analog-to-digital converters (ADCs), and a neural circuit. The first DAC is configured to convert a digital input signal into an analog input signal. The second DAC is configured to convert a digital previous hidden state (PHS) signal into an analog PHS signal. NVM weight arrays are configured to compute vector matrix multiplication (VMM) arrays based on the analog input signal and the analog PHS signal. The NVM weight arrays are coupled to the first DAC and the second DAC. The one or more ADCs are coupled to the plurality of NVM weight arrays and are configured to convert the VMM arrays into digital VMM values. The neural circuit is configured to process the digital VMM values into a new hidden state.

Abstract: A method for of encoding an application screen comprises partitioning graphic data into a plurality of graphic layers and classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. The method further comprises classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. Further, the method comprises rendering and encoding the one or more SC layers using a first codec and the one or more non-SC layers using a second codec.

Abstract: Transcoding bitrate prediction techniques can include receiving a first encoded content. A transcoder bitrate can be estimated based on regression over a video quality estimator of the first encoded content and a second encoded content. The estimated transcoder bitrate can be utilized to transcoding the first encoded content into the second encoded.

Abstract: Methods and apparatuses for video processing based on spatial or temporal importance include: in response to receiving picture data of a picture of a video sequence, determining a level of semantic importance for the picture data, the picture data including a portion of the picture; and applying to the picture data a first resolution-enhancement technique associated with the level of semantic importance for increasing resolution of the picture data, wherein the first resolution-enhancement technique is selected from a set of resolution-enhancement techniques having different computational complexity levels.

Abstract: Scene aware video content encoding techniques can determine if video content is a given content type and is one of one or more given titles that include one or more given scenes. The one or more given scenes of the video content of the given type and given one of the titles can be encoded using corresponding scenes specific encoding parameter values, and the non-given scenes can be encoded using one or more general encoding parameter values. The one or more given titles can be selected based on a rate of streaming of various video content titles of the given type.

Abstract: A video processing unit can include a non-object-based region-of-interest detection neural network, a threshold selection module and a region-of-interest map generator. The non-object-based region-of-interest detection neural network can be configured to receive a video frame and generate a plurality of candidate non-object-based region-of-interest blocks. The threshold selection module can be configured to receive the plurality of candidate non-object-based region-of-interest blocks and identify a plurality of selected region-of-interest blocks based on a predetermined threshold. The region-of-interest map generator can be configured to receive the selected non-object-based region-of-interest blocks and generate a region-of-interest map.

Abstract: Video coding techniques including differential bit rate or quality coding of one or more regions of interest and one or more non-regions of interest based on information including one or more of coordinates of the one or more regions of interest, a target complexity, residual encoder bit data, a requested quality, a difference between the current video data frame and a reconstructed video data frame, a target quality, a requested bit rate, frame target bit allocation and an as encoded bit rate.