Apparatus, a method and a computer program for video coding and decoding

1 . An apparatus comprising:

at least one processor; and

at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:

obtain an image block unit comprising samples in color channels of one or two chrominance channels and one luminance channel;

reconstruct samples of said one luminance channel of the image block unit;

predict the samples of at least one color channel of the image block unit using a cross-component prediction model based on reconstructed samples on a reference channel, wherein parameters of the cross-component prediction model are determined at least partly based on a plurality of reconstructed chrominance samples and the corresponding reconstructed luminance samples in neighboring image block units;

predict a plurality of color channel samples in said neighboring image block units co-locating at least part of the reconstructed chrominance and luminance samples using the cross-component prediction model;

determine a first difference between values of the predicted plurality of color channel samples in said neighboring image block units and corresponding reconstructed color channel samples;

adjust one or more of the parameters of the cross-component prediction model based on said first difference;

select a different set of reconstructed chrominance and luminance samples in said neighboring image block units;

predict a plurality of chrominance channel samples in said neighboring image block units co-locating at least partly to said reconstructed chrominance and luminance samples using said cross-component prediction model with the adjusted parameters; and

determine a second difference between values of the predicted different set of chrominance channel samples in said neighboring image block units and corresponding reconstructed chrominance channel samples.

2 . The apparatus according to claim 1 , wherein the reference channel is a luminance channel.

3 . The apparatus according to claim 2 , wherein the apparatus is further caused to:

in response to the second difference being larger than the first difference, select a further different set of reconstructed chrominance and luminance samples in said neighboring image block units and repeat previous steps; and

in response to the second difference being smaller than the first difference, use said cross-component prediction model with the adjusted parameters for predicting color samples of a current image block unit.

4 . The apparatus according to claim 3 , wherein the selection of a further different set of reconstructed chrominance and luminance samples in said neighboring image block units is configured to repeat previous steps until the second difference reaches a predetermined value; and

said cross-component prediction model with the adjusted parameters is configured to use parameters based on the second difference reaching the predetermined value.

5 . The apparatus according to claim 2 , wherein the apparatus is further caused to:

add a delta value to the sample values of at least one set of samples used in deriving the parameters; and

use said cross-component prediction model with the adjusted parameters added with the delta value providing a sufficiently small second difference.

6 . The apparatus according to claim 2 , wherein the apparatus is further caused to:

add a delta value to the parameters of the cross-component prediction model; and

use said cross-component prediction model with the adjusted parameters added with the delta value providing a sufficiently small second difference.

7 . The apparatus according to claim 1 , wherein the reference channel is a chrominance channel.

8 . The apparatus according to claim 7 , wherein the apparatus is further caused to:

calculate inverted parameters of the cross-component prediction model, wherein an inverted cross-component prediction model uses samples from at least one chrominance channel for predicting samples of a luminance channel; and

predict a plurality of luminance channel samples in said image block unit using said inverted cross-component prediction model.

9 . The apparatus according to claim 1 , wherein the apparatus is further caused to:

multiply the set of samples used in deriving the parameters by an integer number or a fractional number.

10 . The apparatus according to claim 1 , wherein the apparatus is further caused to:

use one or more cross-component models for predicting the samples of the image block unit.

11 . The apparatus according to claim 10 , wherein the cross-component prediction model comprises a linear function.

12 . The apparatus according to claim 10 , wherein the cross-component prediction model comprises a polynomial function.

13 . The apparatus according to claim 12 , wherein the cross-component prediction model comprises one or more additional data-dependent terms.

14 . The apparatus according to claim 10 , wherein the parameters of the cross-component prediction model are configured to be derived according to at least one of the following:

co-located luminance blocks or co-located chrominance blocks, in one or more reference frames when coding a chrominance block in inter frame.

15 . A method comprising:

obtaining an image block unit comprising samples in color channels of one or two chrominance channels and one luminance channel;

reconstructing samples of said one luminance channel of the image block unit;

predicting the samples of at least one color channel of image block unit using a cross-component prediction model based on reconstructed samples on a reference channel, wherein parameters of the cross-component prediction model are determined at least partly based on a plurality of reconstructed chrominance samples and the corresponding reconstructed luminance samples in neighboring image block units;

predicting a plurality of color channel samples in said neighboring image block units co-locating at least part of the reconstructed chrominance and luminance samples using said cross-component prediction model;

determining a first difference between values of the predicted plurality of color channel samples in said neighboring image block units and corresponding reconstructed color channel samples;

adjusting the parameters of the cross-component prediction model based on said difference;

selecting a different set of reconstructed chrominance and luminance samples in said neighboring image block units;

predicting a plurality of chrominance channel samples in said neighboring image block units co-locating at least partly to said reconstructed chrominance and luminance samples using said cross-component prediction model with the adjusted parameters; and

determining a second difference between values of the predicted different set of chrominance channel samples in said neighboring image block units and corresponding reconstructed chrominance channel samples.

16 . The method according to claim 15 , wherein the reference channel is a luminance channel.

17 . The method according to claim 16 further comprising:

in response to the second difference being larger than the first difference, selecting a further different set of reconstructed chrominance and luminance samples in said neighboring image block units and repeat previous steps; and

in response to the second difference being smaller than the first difference, using said cross-component prediction model with the adjusted parameters for predicting color samples of a current image block unit.

18 . The method according to claim 17 further comprising:

configuring the selection of a further different set of reconstructed chrominance and luminance samples in said neighboring image block units to repeat previous steps until the second difference reaches a predetermined value; and

configuring said cross-component prediction model with the adjusted parameters to use parameters based on the second difference reaching the predetermined value.

19 . The method according to claim 15 , wherein the reference channel is a chrominance channel.

20 . A non-transitory computer readable medium computer program comprising program instructions which when executed by an apparatus, cause the apparatus at least to:

obtain an image block unit comprising samples in color channels of one or two chrominance channels and one luminance channel;

reconstruct samples of said one luminance channel of the image block unit;

predict the samples of at least one color channel of image block unit using a cross-component prediction model based on reconstructed samples on a reference channel, wherein parameters of the cross-component prediction model are determined at least partly based on a plurality of reconstructed chrominance samples and the corresponding reconstructed luminance samples in neighboring image block units;

predict a plurality of color channel samples in said neighboring image block units co-locating at least part of the reconstructed chrominance and luminance samples using said cross-component prediction model;

determine a first difference between values of the predicted plurality of color channel samples in said neighboring image block units and corresponding reconstructed color channel samples; and

adjust one or more of the parameters of the cross-component prediction model based on said first difference;

select a different set of reconstructed chrominance and luminance samples in said neighboring image block units;

predict a plurality of chrominance channel samples in said neighboring image block units co-locating at least partly to said reconstructed chrominance and luminance samples using said cross-component prediction model with the adjusted parameters; and

determine a second difference between values of the predicted different set of chrominance channel samples in said neighboring image block units and corresponding reconstructed chrominance channel samples.