A disclosed method may include storing, within a hardware memory device included as part of a rate—distortion optimization (RDO) hardware pipeline, at least one transform unit table that (1) is pregenerated from a seed probability table for transformation of video data in accordance with a video encoding standard, (2) corresponds to a transform operation supported by the video encoding standard, and (3) corresponds to a transform unit included in the RDO hardware pipeline. The method may also include determining, by accessing the transform unit table, an RDO token rate for an encoding of the video data by a hardware video encoding pipeline that includes the RDO hardware pipeline, and selecting, based on the RDO token rate, a transform operation for the encoding of the video data.

A disclosed method may include storing, within a hardware memory device included as part of a rate—distortion optimization (RDO) hardware pipeline, at least one transform unit table that (1) is pregenerated from a seed probability table for transformation of video data in accordance with a video encoding standard, (2) corresponds to a transform operation supported by the video encoding standard, and (3) corresponds to a transform unit included in the RDO hardware pipeline. The method may also include determining, by accessing the transform unit table, an RDO token rate for an encoding of the video data by a hardware video encoding pipeline that includes the RDO hardware pipeline, and selecting, based on the RDO token rate, a transform operation for the encoding of the video data.

Disclosed herein are a video-decoding method and apparatus and a video encoding method and apparatus, and more particularly a method and an apparatus which perform region-differential image encoding/decoding using a recovered image. In accordance with an encoding method according to an embodiment, a recovered low-quality image is generated by performing encoding on an original image and a recovered high-quality image is generated using the recovered low-quality image. An image is segmented into multiple regions, and encoded reconstruction information for generating a reconstructed high-quality image is generated by performing encoding on the image.

Disclosed herein are a video-decoding method and apparatus and a video encoding method and apparatus, and more particularly a method and an apparatus which perform region-differential image encoding/decoding using a recovered image. In accordance with an encoding method according to an embodiment, a recovered low-quality image is generated by performing encoding on an original image and a recovered high-quality image is generated using the recovered low-quality image. An image is segmented into multiple regions, and encoded reconstruction information for generating a reconstructed high-quality image is generated by performing encoding on the image.

A disclosed system may include a hardware distortion data pipeline that may include (1) a quantization module that generates a quantized data set, (2) an inverse quantization module that generates, from the quantized data set, an inverse quantized data set by executing an inverse quantization of the quantized data set, and (3) an inverse transformation module that generates an inversely transformed data set by executing an inverse transformation of the inverse quantized data set. The system may also include a hardware determination pipeline that determines a distortion metric based on the inversely transformed data set and the residual frame data set, and a hardware token rate pipeline that determines, based on the quantized data set, a token rate for an encoding of the residual frame data set via a video encoding pipeline. Various other methods, systems, and computer-readable media are also disclosed.

A disclosed system may include a hardware distortion data pipeline that may include (1) a quantization module that generates a quantized data set, (2) an inverse quantization module that generates, from the quantized data set, an inverse quantized data set by executing an inverse quantization of the quantized data set, and (3) an inverse transformation module that generates an inversely transformed data set by executing an inverse transformation of the inverse quantized data set. The system may also include a hardware determination pipeline that determines a distortion metric based on the inversely transformed data set and the residual frame data set, and a hardware token rate pipeline that determines, based on the quantized data set, a token rate for an encoding of the residual frame data set via a video encoding pipeline. Various other methods, systems, and computer-readable media are also disclosed.

A video to be encoded to a plurality of different target encodings for bandwidth adaptive serving is received. The video is encoded into a plurality of different candidate encodings using different candidate encoding parameters. A quality metric is determined for each of the plurality of different candidate encodings. One or more different target quality metrics are selected for a first portion of the different target encodings based at least in part on one or more specified constraints for one or more target devices. One or more different target quality metrics are selected for a second portion of the different target encodings based at least in part on the determined quality metrics of the different candidate encodings. Based at least in part on the selected different target quality metrics for the first portion and the second portion, the plurality of different target encodings of the video is generated.

Systems and methods are described for video coding using affine motion models with adaptive precision. In an example, a block of video is encoded in a bitstream using an affine motion model, where the affine motion model is characterized by at least two motion vectors. A precision is selected for each of the motion vectors, and the selected precisions are signaled in the bitstream. In some embodiments, the precisions are signaled by including in the bitstream information that identifies one of a plurality of elements in a selected predetermined precision set. The identified element indicates the precision of each of the motion vectors that characterize the affine motion model. In some embodiments, the precision set to be used is signaled expressly in the bitstream; in other embodiments, the precision set may be inferred, e.g., from the block size, block shape or temporal layer.

Certain aspects of the present disclosure provide techniques for encoding image data for one or more images. In one embodiment, a method includes the steps of downscaling the one or more images, and encoding the one or more downscaled images using an image codec. Another embodiment concerns a computer-implemented method of decoding encoded image data, and a computer-implemented method of encoding and decoding image data.

A set of rough prediction modes including a MPM subset is determined for a code block during image encoding. A first prediction mode having a mode cost less than a first threshold is selected from the set of rough prediction modes, and a second prediction mode having a mode cost less than a second threshold is selected from the MPM subset. A candidate mode subset is determined based on mode types of prediction modes contained in the set of rough prediction modes and a ranking result of mode costs of the prediction modes, when the mode cost of the first prediction mode is different from the mode cost of the second prediction mode. A target prediction mode is determined for the code block from the candidate mode subset. The code block is encoded with the target prediction mode.