Cross component intra prediction mode
A method, computer program, and computer system is provided for encoding video data. Data corresponding to a video frame is received. One or more luma and chroma samples from are identified from the received image data. Two or more linear models are determined from the identified luma and chroma samples. The luma and chroma samples are classified into a number of categories corresponding to a number of linear models, and each category has one or more corresponding linear model parameters signaled to the bitstream.
1. A method of video encoding, executable by a processor, the method comprising:
identifying one or more luma samples and one or more chroma samples from image data, wherein the one or more chroma samples are predicted from the one or more luma samples based on a DC contribution from a chroma component of the image data and an AC contribution from a luma component of the image data; and
determining two or more linear models from the identified one or more luma samples and the identified one or more chroma samples, wherein the one or more luma samples and the one or more chroma samples are classified into a number of categories corresponding to a number of linear models, and wherein each category has one or more corresponding linear model parameters signaled to a bitstream.
2. The method of claim 1 , wherein the one or more chroma samples are classified into one or more models based on coordinates associated with the one or more chroma samples.
3. The method of claim 2 , wherein chroma samples corresponding to even rows or columns are classified into a first category and chroma samples corresponding to odd rows or columns are classified into a second category.
4. The method of claim 1 , wherein the one or more chroma samples are classified into the linear models based on one or more values of corresponding reconstructed luma samples.
5. The method of claim 1 , wherein parameters of a first linear model from among the linear models are directly signaled, and a difference between one or more of the parameters and the first linear model are signaled for remaining linear models.
6. The method of claim 1 , wherein one or more average luma values for each linear model are computed by using samples belonging to the each category.
7. The method of claim 1 , wherein a first flag signals the linear models and a status corresponding to a chroma-from-luma mode are in a cross-component mode and a second flag signals whether a current mode corresponds to the linear models or the chroma-from-luma mode.
8. The method of claim 1 , wherein one or more luma samples outside of boundaries associated with the image data are not used for calculating average luma values for a chroma-from-luma mode.
9. The method of claim 1 , wherein one or more average luma values are computed by using the one or more luma samples in one or multiple predefined positions when a chroma-from-luma mode is used.
10. The method of claim 1 , wherein one or predicted linear model parameters are derived using neighboring reconstructed luma and chroma samples.
11. A computer system for encoding video data, the computer system comprising:
one or more computer-readable non-transitory storage media configured to store computer program code; and
one or more computer processors configured to access said computer program code and operate as instructed by said computer program code, said computer program code including:
identifying code configured to cause the one or more computer processors to identify one or more luma samples and one or more chroma samples from image data, wherein the one or more chroma samples are predicted from the one or more luma samples based on a DC contribution from a chroma component of the image data and an AC contribution from a luma component of the image data; and
determining code configured to cause the one or more computer processors to determine two or more linear models from the identified one or more luma samples and the identified one or more chroma samples, wherein the one or more luma and the one or more chroma samples are classified into a number of categories corresponding to a number of linear models, and wherein each category has one or more corresponding linear model parameters signaled to a bitstream.
12. The computer system of claim 11 , wherein the one or more chroma samples are classified into one or more models based on coordinates associated with the one or more chroma samples.
13. The computer system of claim 12 , wherein chroma samples corresponding to even rows or columns are classified into a first category and chroma samples corresponding to odd rows or columns are classified into a second category.
14. The computer system of claim 11 , wherein the one or more chroma samples are classified into the linear models based on one or more values of corresponding reconstructed luma samples.
15. The computer system of claim 11 , wherein parameters of a first linear model from among the linear models are directly signaled, and a difference between one or more of the parameters and the first linear model are signaled for remaining linear models.
16. The computer system of claim 11 , wherein one or more average luma values for each linear model are computed by using samples belonging to the each category.
17. The computer system of claim 11 , wherein a first flag signals the linear models and a status corresponding to a chroma-from-luma mode are in a cross-component mode and a second flag signals whether a current mode corresponds to the linear models or the chroma-from-luma mode.
18. The computer system of claim 11 , wherein one or more luma samples outside of boundaries associated with the image data are not used for calculating average luma values for a chroma-from-luma mode.
19. The computer system of claim 11 , wherein one or more average luma values are computed by using the one or more luma samples in one or multiple predefined positions when a chroma-from-luma mode is used.
20. A non-transitory computer readable medium having stored thereon a computer program for encoding video data, the computer program configured to cause one or more computer processors to:
identify one or more luma samples and one or more chroma samples from image data, wherein the one or more chroma samples are predicted from the one or more luma samples based on a DC contribution from a chroma component of the image data and an AC contribution from a luma component of the image data; and
determine two or more linear models from the identified one or more luma samples and the identified one or more chroma samples, wherein the one or more luma and the one or more chroma samples are classified into a number of categories corresponding to a number of linear models, and wherein each category has one or more corresponding linear model parameters signaled to a bitstream.