IMPROVING MULTISCALE RECURRENT PATTERN IMAGE CODINGWITH LEAST-SQUARES PREDICTION MODE

The Multidimensional Multiscale Parser-based (MMP) image cod-ing algorithm, when combined with flexible partitioning and predic-tive coding techniques (MMP-FP), provides state-of-the-art perfor-mance. In this paper we investigate the use of adaptive least-squares prediction in MMP. The linear prediction coefficients implicitly embed the local tex-ture characteristics, and are computed based on a block’s causal neighborhood (composed of already reconstructed data). Thus, the intra prediction mode is adaptively adjusted according to the local context and no extra overhead is needed for signaling the coeffi-cients. We add this new context-adaptive linear prediction mode to the other MMP prediction modes, that are based on the ones used in H.264/AVC; the best mode is chosen through rate-distortion opti-mization. Simulation results show that least-squares prediction is able to significantly increase MMP-FPs rate-distortion performance for smooth images, leading to better results than the ones of state-of-the-art, transform-based methods. Yet with the addition of least-squares prediction MMP-FP presents no performance loss when used for encoding non-smooth images, such as text and graphics