Abstract:
Techniques and systems are provided for compressing data in a neural network. For example, output data can be obtained from a node of the neural network. Re-arranged output data having a re-arranged scanning pattern can be generated. The re-arranged output data can be generated by re-arranging the output data into the re-arranged scanning pattern. One or more residual values can be determined for the re-arranged output data by applying a prediction mode to the re-arranged output data. The one or more residual values can then be compressed using a coding mode.
Abstract:
Systems and methods for low complexity forward transforms using zeroed-out coefficients are described herein. One aspect of the subject matter described in the disclosure provides a video encoder comprising a memory configured to store a video block. The video encoder further comprises a processor in communication with the memory. The processor is configured to determine a full power value of the video block. The processor is further configured to determine a reduced transform coefficient matrix, wherein the reduced transform coefficient matrix comprises an inner region of zero or non-zero values of the same inner region of a full transform coefficient matrix and an outer region of zero values, wherein the reduced transform coefficient matrix and the full transform coefficient matrix have the same size. The processor is further configured to determine a partial power value of the video block using the reduced transform coefficient matrix. The processor is further configured to transform the video block from a pixel domain to a coefficient domain using the reduced transform coefficient matrix based on the full power value and partial power value. The processor is further configured to encode the transformed video block.
Abstract:
A video encoder generates a sequence of sample adaptive offset (SAO) syntax elements for a coding tree block. The SAO syntax elements include regular context-adaptive binary arithmetic coding (CABAC) coded bins for a color component and bypass-coded bins for the color component. None of the bypass-coded bins is between two of the regular CABAC-coded bins in the sequence. The video encoder uses regular CABAC to encode the regular CABAC-coded bins and uses bypass coding to encode the bypass-coded bins. The video encoder outputs the SAO syntax elements in a bitstream. A video decoder receives the bitstream, uses regular CABAC to decode the regular CABAC-coded bins, uses bypass coding to decode the bypass-coded bins, and modifies a reconstructed picture based on the SAO syntax elements.
Abstract:
Certain aspects of the present disclosure provide a method of encoding data. The method generally includes receiving data comprising a fractional number comprising an exponential component and a fractional component, the exponential component being represented by an exponential bit sequence, the fractional component being represented by a fractional bit sequence. The method further includes determining if the fractional component is within a threshold of 0 or 1. The method further includes setting the fractional component to 0 when the fractional component is within the threshold of 0 or 1. The method further includes downscaling the fractional bit sequence based on a difference between the exponential component and a second threshold. The method further includes encoding the data. The method further includes transmitting the encoded data.
Abstract:
Techniques and systems are provided for compressing data in a neural network. For example, output data can be obtained from a node of the neural network. Re-arranged output data having a re-arranged scanning pattern can be generated. The re-arranged output data can be generated by re-arranging the output data into the re-arranged scanning pattern. One or more residual values can be determined for the re-arranged output data by applying a prediction mode to the re-arranged output data. The one or more residual values can then be compressed using a coding mode.
Abstract:
Systems and methods for low complexity forward transforms using mesh-based calculations are described herein. One aspect of the subject matter described in the disclosure provides a video encoder comprising a memory configured to store video information. The video encoder further comprises a processor in communication with the memory. The processor is configured to decompose a transform into multiple transform stages. The processor is further configured to transform the video information using the multiple stages to determine a transform stage output at each transform stage. The processor is further configured to constrain the transform stage output at each transform stage to a predetermined bit depth. The processor is further configured to perform operations on the constrained transform output of a last stage of the multiple stages, wherein the operations are only available for use with data having the predetermined bit depth.
Abstract:
Systems and methods for low complexity encoding and background detection are described herein. One aspect of the subject matter described in the disclosure provides a video encoder comprising a memory configured to store a video block. The video encoder further comprises a processor in communication with the memory. The processor is configured to determine whether the video block is background by comparing the video block to a corresponding block located in a previous temporal frame. The processor is further configured to determine, when the video block is not background, whether one or more sub-blocks of the video block are background by comparing the sub-blocks to corresponding sub-blocks located in the previous temporal frame.