Perceptual Rate-Distortion Optimization Using Structural Similarity Index as Quality Metric

Abstract

The rate-distortion optimization (RDO) framework for video coding achieves a tradeoff between bit-rate and quality. However, objective distortion metrics such as mean squared error traditionally used in this framework are poorly correlated with perceptual quality. We address this issue by proposing an approach that incorporates the structural similarity index as a quality metric into the framework. In particular, we develop a predictive Lagrange multiplier estimation method to resolve the chicken and egg dilemma of perceptual-based RDO and apply it to H.264 intra and inter mode decision. Given a perceptual quality level, the resulting video encoder achieves on the average 9% bit-rate reduction for intra-frame coding and 11% for inter-frame coding over the JM reference software. Subjective test further confirms that, at the same bit-rate, the proposed perceptual RDO indeed preserves image details and prevents block artifact better than traditional RDO.