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:
Techniques are described for providing continuous control of a deblocking filter for a video block using a beta offset parameter. Deblocking filters are defined based on one or more deblocking decisions. Conventionally, a quantization parameter and a beta offset parameter are used to identify a beta parameter (“β”) value that determines threshold values of the deblocking decisions. The value of the beta offset parameter results in a change or increment of the β value. For small increments of the β value, rounding of the threshold values may result in no change and discontinuous control of the deblocking decisions. The techniques include calculating at least one deblocking decision for the deblocking filter according to a threshold value that has been modified based on a multiplier value of the beta offset parameter. The multiplier value applied to the beta offset parameter causes an integer change in the modified threshold value.
Abstract:
In some embodiments of a video coder, if some prediction information is not available for a first block in a current layer, the video coder uses corresponding information (e.g., intra prediction direction and motion information), if available, from the first block's co-located second block in the base layer as if it were the prediction information for the first block. The corresponding information can then be used in the current layer to determine the prediction information of succeeding blocks in the current layer.
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:
An apparatus for coding video data using a single-loop decoding approach may include a memory unit and a processor in communication with the memory unit. In an embodiment, the memory unit stores the video data, the video data including a base layer and an enhancement layer. The base layer includes a base layer block, a non-constrained INTRA mode block, and an INTER mode block. The base layer block includes a sub-block located at least partially within one of the non-constrained INTRA mode block or the INTER mode block. The enhancement layer includes an enhancement layer block located at a position in the enhancement layer corresponding to a position of the base layer block in the base layer. The processor approximates pixel values of the sub-block and determines, based at least in part on the approximated pixel values, pixel values of the enhancement layer block.
Abstract:
A video decoder determines, based at least in part on a size of a prediction unit (PU), whether to round either or both a horizontal or a vertical component of a motion vector of the PU from sub-pixel accuracy to integer-pixel accuracy. The video decoder generates, based at least in part on the motion vector, a predictive sample block for the PU and generates, based in part on the predictive sample block for the PU, a reconstructed sample block.
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:
A method of decoding video data includes receiving syntax elements extracted from an encoded video bitstream, determining a candidate list for an enhancement layer block, and selectively pruning the candidate list. The syntax elements include information associated with a base layer block of a base layer of the video data. The candidate list is determined at least in part on motion information associated with the base layer block. The enhancement layer block is in an enhancement layer of the video data. The candidate list includes at least one motion information candidate that includes the motion information associated with the base layer block. The candidate list includes a merge list or an AMVP list. Pruning includes comparing one or more motion information candidates and at least one motion information candidate associated with the base layer block that is in the candidate list.