Abstract: For a picture with two or more color components, the prediction residuals for the first color component of a block to be encoded may be transformed with a first transform. The transform coefficients for the first color component may go through quantization, de-quantization and inverse transform to obtain reconstructed prediction residuals. Based on the reconstructed prediction residuals for the first color component, the phases of the transform basis function of the first transform can be adjusted to improve the sparsity of the transformed signal. The prediction residuals for the remaining color components may then be transformed with the adjusted transform. In order to determine the phase shift factor, the reconstructed prediction residuals for the first color component may be transformed with the first transform, adjusted by different candidate phase shift factors, and the candidate phase shift factor that provides a smallest sparsity measure can be selected for the block.

Abstract: For a picture with two or more color components, the prediction residuals for the first color component of a block to be encoded may be transformed with a first transform. The transform coefficients for the first color component may go through quantization, de-quantization and inverse transform to obtain reconstructed prediction residuals. Based on the reconstructed prediction residuals for the first color component, the phases of the transform basis function of the first transform can be adjusted to improve the sparsity of the transformed signal. The prediction residuals for the remaining color components may then be transformed with the adjusted transform. In order to determine the phase shift factor, the reconstructed prediction residuals for the first color component may be transformed with the first transform, adjusted by different candidate phase shift factors, and the candidate phase shift factor that provides a smallest sparsity measure can be selected for the block.

Abstract: An aspect of present principles is directed to methods, apparatus, systems and computer readable media for image processing. The image processing may include receiving a standard dynamic range (SDR) image or a receiver for receiving a standard dynamic range (SDR) image. It may further include determining an over-exposed region of the SDR image and determining a corrected luminance value for at least a pixel of the over-exposed region, or a processor configured to determine an over-exposed region of the SDR image and a corrected luminance value for at least a pixel of the over-exposed region. The corrected luminance value is determined based on at least one of a shape of the over-exposed region, luminance information of pixels surrounding the over-exposed region, and edge information of the pixels surrounding the over-exposed region. The over-exposed region may be an irregularly shaped region.

Abstract: An aspect of present principles is directed to methods, apparatus, systems and computer readable media for image processing. The image processing may include receiving a standard dynamic range (SDR) image or a receiver for receiving a standard dynamic range (SDR) image. It may further include determining an over-exposed region of the SDR image and determining a corrected luminance value for at least a pixel of the over-exposed region, or a processor configured to determine an over-exposed region of the SDR image and a corrected luminance value for at least a pixel of the over-exposed region. The corrected luminance value is determined based on at least one of a shape of the over-exposed region, luminance information of pixels surrounding the over-exposed region, and edge information of the pixels surrounding the over-exposed region. The over-exposed region may be an irregularly shaped region.