METHOD AND APPARATUS FOR VIDEO ENCODING AND DECODING WITH SUBBLOCK BASED LOCAL ILLUMINATION COMPENSATION
Different implementations are described, particularly implementations for video encoding and decoding based on a linear model responsive to neighboring samples are presented. Accordingly, for a block being encoded or decoded in a picture, refined linear model parameters are determined for a current subblock in the block and for encoding the block, the local illumination compensation uses a linear model for the current subblock based on the refined linear model parameters. In a first embodiment, the number N of reconstructed samples increases with the available data for the subblock. In a second embodiment, partial linear model parameters are determined for the subblock and refined linear model parameters are derived from a weighted sums of partial linear model parameters. In a third embodiment, the subblocks are independently LIC processed.
1 . A method for video encoding, comprising:
determining, for a block being encoded in a picture, model parameters for a local illumination compensation based on spatially neighboring reconstructed samples and corresponding reference samples;
encoding the block using local illumination compensation based on the determined model parameters;
wherein the block is partitioned into a plurality of subblocks;
wherein determining the model parameters for the block comprises determining refined model parameters for a current subblock in the block;
and wherein for encoding the block, the local illumination compensation uses a model for the current subblock based on the refined model parameters.
2 . A method for video decoding, comprising:
determining, for a block being decoded in a picture, model parameters for a local illumination compensation based on spatially neighboring reconstructed samples and corresponding reference samples;
decoding the block using local illumination compensation based on the determined model parameters;
wherein the block is partitioned into a plurality of subblocks;
wherein determining the model parameters for the block comprises determining refined model parameters for a current subblock in the block;
and wherein for decoding the block, the local illumination compensation uses a model for the current subblock based on the refined model parameters.
3 . An apparatus for video encoding, comprising one or more processors, and at least one memory and wherein the one or more processors is configured to:
determine, for a block being encoded in a picture, model parameters for a local illumination compensation based on spatially neighboring reconstructed samples and corresponding reference samples;
encode the block using local illumination compensation based on the determined model parameters;
wherein the block is partitioned into a plurality of subblocks;
wherein the model parameters for the block are determined by determining refined model parameters for a current subblock in the block;
and wherein to encode the block, the local illumination compensation uses a model for the current subblock based on the refined model parameters.
4 . An apparatus for video decoding, comprising one or more processors, and at least one memory and wherein the one or more processors is configured to:
determine, for a block being decoded in a picture, model parameters for a local illumination compensation based on spatially neighboring reconstructed samples and corresponding reference samples;
decode the block using local illumination compensation based on the determined model parameters;
wherein the blocks is partitioned into a plurality of subblocks;
wherein the model parameters for the block are determined by determining refined model parameters for a current subblock in the block:
and wherein to decode the block, the local illumination compensation uses a model for the current subblock based on the refined model parameters.
5 . The method of claim 1 , wherein determining the refined model parameters for a current subblock comprises:
accessing spatially neighboring reconstructed samples of the current subblock and corresponding reference samples;
determining the refined model parameters based on previously accessed spatially neighboring reconstructed samples and corresponding reference samples for the block.
6 . The method of claim 5 , wherein determining the refined model parameters for a current subblock comprises determining the refined model parameters based on all previously accessed spatially neighboring reconstructed samples and corresponding reference samples for the block.
7 . (canceled)
8 . The method of claim 5 , determining the refined model parameters for a current subblock comprises determining the refined model parameters based on previously accessed spatially neighboring reconstructed samples and corresponding reference samples closest to samples of the current subblock.
9 . (canceled)
10 . The method of claim 5 , wherein determining the refined model parameters for a current subblock comprises
processing partial sums from the accessed spatially neighboring reconstructed samples of the current subblock and corresponding reference samples;
storing partial sums for the current subblock into a buffer of partial sums for the block and;
determining the refined model parameters based on stored partial sums.
11 . The method of claim 1 , wherein determining the refined model parameters for a current subblock comprises:
determining partial model parameters based on the spatially neighboring reconstructed samples and corresponding reference samples for a current subblock;
determining refined model parameters from a weighted sum of the previously determined partial model parameters for the subblocks.
12 - 15 . (canceled)
16 . The method of claim 2 , wherein determining the refined model parameters for a current subblock comprises:
accessing spatially neighboring reconstructed samples of the current subblock and corresponding reference samples;
determining the refined model parameters based on previously accessed spatially neighboring reconstructed samples and corresponding reference samples for the block.
17 . The method of claim 16 , wherein determining the refined model parameters for a current subblock comprises determining the refined model parameters based on all previously accessed spatially neighboring reconstructed samples and corresponding reference samples for the block.
18 . The method of claim 16 , wherein determining the refined model parameters for a current subblock comprises determining the refined model parameters based on previously accessed spatially neighboring reconstructed samples and corresponding reference samples closest to samples of the current subblock.
19 . The method of claim 16 , wherein determining the refined model parameters for a current subblock comprises
processing partial sums from the accessed spatially neighboring reconstructed samples of the current subblock and corresponding reference samples;
storing partial sums for the current subblock into a buffer of partial sums for the block and;
determining the refined model parameters based on stored partial sums.
20 . The method of claim 2 , wherein determining the refined model parameters for a current subblock comprises:
determining partial model parameters based on the spatially neighboring reconstructed samples and corresponding reference samples for a current subblock;
determining refined model parameters from a weighted sum of the previously determined partial model parameters for the subblocks.
21 . The apparatus of claim 3 , wherein the one or more processors is further configured to:
access spatially neighboring reconstructed samples of the current subblock and corresponding reference samples;
determine the refined model parameters based on previously accessed spatially neighboring reconstructed samples and corresponding reference samples for the block.
22 . The apparatus of claim 21 , wherein the one or more processors is configured to determine the refined model parameters based on all previously accessed spatially neighboring reconstructed samples and corresponding reference samples for the block.
23 . The apparatus of claim 21 , wherein the one or more processors is configured to determine the refined model parameters for a current subblock based on previously accessed spatially neighboring reconstructed samples and corresponding reference samples closest to samples of the current subblock.
24 . The apparatus of claim 21 , wherein the one or more processors is configured to:
process partial sums from the accessed spatially neighboring reconstructed samples of the current subblock and corresponding reference samples;
store partial sums for the current subblock into a buffer of partial sums for the block and;
determine the refined model parameters based on stored partial sums.
25 . The apparatus of claim 3 , wherein the one or more processors is configured to:
determine partial model parameters based on the spatially neighboring reconstructed samples and corresponding reference samples for a current subblock;
determine refined model parameters from a weighted sum of the previously determined partial model parameters for the subblocks.
26 . The apparatus of claim 4 , wherein the one or more processors is further configured to:
access spatially neighboring reconstructed samples of the current subblock and corresponding reference samples;
determine the refined model parameters based on previously accessed spatially neighboring reconstructed samples and corresponding reference samples for the block.
27 . The apparatus of claim 26 , wherein the one or more processors is configured to determine the refined model parameters based on all previously accessed spatially neighboring reconstructed samples and corresponding reference samples for the block.
28 . The apparatus of claim 26 , wherein the one or more processors is configured to determine the refined model parameters for a current subblock based on previously accessed spatially neighboring reconstructed samples and corresponding reference samples closest to samples of the current subblock.
29 . The apparatus of claim 26 , wherein the one or more processors is configured to:
process partial sums from the accessed spatially neighboring reconstructed samples of the current subblock and corresponding reference samples;
store partial sums for the current subblock into a buffer of partial sums for the block and;
determine the refined model parameters based on stored partial sums.
30 . The apparatus of claim 4 , wherein the one or more processors is configured to:
determine partial model parameters based on the spatially neighboring reconstructed samples and corresponding reference samples for a current subblock;
determine refined model parameters from a weighted sum of the previously determined partial model parameters for the subblocks.