The Multidimensional Multiscale Parser-based (MMP) image coding algorithm, when combined with flexible partitioning and predictive coding techniques (MMP-FP), provides state-of-the-art performance.
In this paper we investigate the use of adaptive least-squares
prediction in MMP.
The linear prediction coefficients implicitly embed the local texture
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 coefficients.
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 optimization.
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-theart, 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.