A method and apparatus for reducing artifacts in a compressed image using a neural-network based deblocking filter. The method may include receiving at least one reconstructed image, wherein each reconstructed image comprises one or more reconstructed blocks and extracting boundary areas associated with boundaries of the one or more reconstructed blocks in the at least one reconstructed image. The extracted boundary areas may be input in a trained deblocking model to generate boundary areas having reduced artifacts and the trained deblocking mode is trained on training data based on estimated compression by a neural image compression (NIC) network. The edge areas associated with the generated boundary areas may be removed; and at least one reconstructed image with reduced artifacts may be generated based on the generated boundary areas.

A method and apparatus for reducing artifacts in a compressed image using a neural-network based deblocking filter. The method may include receiving at least one reconstructed image, wherein each reconstructed image comprises one or more reconstructed blocks and extracting boundary areas associated with boundaries of the one or more reconstructed blocks in the at least one reconstructed image. The extracted boundary areas may be input in a trained deblocking model to generate boundary areas having reduced artifacts and the trained deblocking mode is trained on training data based on estimated compression by a neural image compression (NIC) network. The edge areas associated with the generated boundary areas may be removed; and at least one reconstructed image with reduced artifacts may be generated based on the generated boundary areas.

A de-blocking filter includes a reconstructed memory that is configured to store reconstructed pixels corresponding to a current macroblock of a video image to be filtered. The current macroblock includes a set of sub-blocks, each sub-block having horizontal edges and vertical edges. An internal pixel buffer in the de-blocking filter is configured to store pixels corresponding to the set of sub-blocks from the reconstructed memory, and to store partially filtered pixels corresponding to a set of partially filtered macroblocks. An edge order controller in the de-blocking filter is configured to load the pixels corresponding to the set of sub-blocks into a filter engine from the internal pixel buffer, to filter the set of sub-blocks, such that, at least one horizontal edge is filtered before filtering all vertical edges of the set of sub-blocks.

A de-blocking filter includes a reconstructed memory that is configured to store reconstructed pixels corresponding to a current macroblock of a video image to be filtered. The current macroblock includes a set of sub-blocks, each sub-block having horizontal edges and vertical edges. An internal pixel buffer in the de-blocking filter is configured to store pixels corresponding to the set of sub-blocks from the reconstructed memory, and to store partially filtered pixels corresponding to a set of partially filtered macroblocks. An edge order controller in the de-blocking filter is configured to load the pixels corresponding to the set of sub-blocks into a filter engine from the internal pixel buffer, to filter the set of sub-blocks, such that, at least one horizontal edge is filtered before filtering all vertical edges of the set of sub-blocks.

A method of processing an image is proposed, which includes: determining, based on the image, one or more noise templates, wherein each of the one or more noise templates includes noise pixels representing noise contained in the image; calculating one or more first autocovariance values, based on the noise pixels of at least one of the one or more noise templates; based on the one or more first autocovariance values, selecting an entry of a noise model database among database entries which respectively include values of noise model parameters corresponding to a noise model.

A method for sample adaptive offset (SAO) filtering and SAO parameter signaling in a video encoder is provided that includes determining SAO parameters for largest coding units (LCUs) of a reconstructed picture, wherein the SAO parameters include an indicator of an SAO filter type and a plurality of SAO offsets, applying SAO filtering to the reconstructed picture according to the SAO parameters, and entropy encoding LCU specific SAO information for each LCU of the reconstructed picture in an encoded video bit stream, wherein the entropy encoded LCU specific SAO information for the LCUs is interleaved with entropy encoded data for the LCUs in the encoded video bit stream. Determining SAO parameters may include determining the LCU specific SAO information to be entropy encoded for each LCU according to an SAO prediction protocol.

A method for sample adaptive offset (SAO) filtering and SAO parameter signaling in a video encoder is provided that includes determining SAO parameters for largest coding units (LCUs) of a reconstructed picture, wherein the SAO parameters include an indicator of an SAO filter type and a plurality of SAO offsets, applying SAO filtering to the reconstructed picture according to the SAO parameters, and entropy encoding LCU specific SAO information for each LCU of the reconstructed picture in an encoded video bit stream, wherein the entropy encoded LCU specific SAO information for the LCUs is interleaved with entropy encoded data for the LCUs in the encoded video bit stream. Determining SAO parameters may include determining the LCU specific SAO information to be entropy encoded for each LCU according to an SAO prediction protocol.

A method and apparatus for encoding or decoding a video sequence includes applying a Cross-Component Linear Model (CCLM) to a video sequence, and applying an interpolation filter in the Cross-Component Linear Model (CCLM), wherein the interpolation filter is dependent upon a YUV format of the video sequence.

A method and apparatus for encoding or decoding a video sequence includes applying a Cross-Component Linear Model (CCLM) to a video sequence, and applying an interpolation filter in the Cross-Component Linear Model (CCLM), wherein the interpolation filter is dependent upon a YUV format of the video sequence.

A method for signalling an intra chroma prediction mode and a method for implementing the signalled intra chroma prediction mode, the intra chroma prediction mode taking an intropolation of previously predicted luma samples from neighboring blocks of video data to attain an intra chroma prediction of a current chroma prediction unit.