Perceptual video coding based on MB classification and rate–distortion optimization

Abstract

The efficiency of a video coding scheme should be defined as the video quality achieved for a given bit-rate. The quality refers here to the subjective quality perceived by the final user. This is the only valid criteria, and it involves the user vision properties. Therefore, in order to optimize this perceived video quality, a new perceptual coding scheme is proposed taking into account the Human Visual System (HVS). Perceptual distortion measures are included in the encoding loop to compute an adaptive local quantization step. The idea is that optimizing the choice of macroblock quantization parameters based on a more accurate perceptual distortion measure would result in an improved subjective quality. The resulting macroblock-level rate allocation problem is first modeled as a constrained optimization problem solved with a Lagrangian multiplier-based algorithm. The local adaptation is performed by a classification of each macroblock according to its distortion–quantization properties. A learning strategy applied on a set of sequences provides a set of representative distortion–quantization curves for all macroblock types. Then a rate–quantization model is derived from the ρ-domain linear approximation to reach the target bit-rate. A two-pass only algorithm is required to compute the necessary models and encode each macroblock. The complexity of the proposed solution is thus reduced compared to exhaustive search algorithms. The SSIM (Structural SIMilarity) is used as in-loop distortion measure, and the performances are evaluated with another perceptual measure, more correlated to the user perception, called the WQA (Wavelet Quality Assessment). The coding schemes being based on a biological possible modeling of the Human Visual System, the video includes less visual artifacts and a more uniform subjective quality, compared to traditional rate–distortion coding.