Modeling Generalized Rate-Distortion Functions

Many multimedia applications require precise understanding of the rate-distortion characteristics measured by the function relating visual quality to media attributes, for which we term it the generalized rate-distortion (GRD) function. In this study, we explore the GRD behavior of compressed digital videos in a two-dimensional space of bitrate and resolution. Our analysis on a large-scale video dataset reveals that empirical parametric models are systematically biased while exhaustive search methods require excessive computation time to depict the GRD surfaces. By exploiting the properties that all GRD functions share, we develop an Robust Axial-Monotonic Clough-Tocher (RAMCT) interpolation method to model the GRD function. This model allows us to accurately reconstruct the complete GRD function of a source video content from a moderate number of measurements. To further reduce the computational cost, we present a novel sampling scheme based on a probabilistic model and an information measure. The proposed sampling method constructs a sequence of quality queries by minimizing the overall informativeness in the remaining samples. Experimental results show that the proposed algorithm significantly outperforms state-of-the-art approaches in accuracy and efficiency. Finally, we demonstrate the usage of the proposed model in three applications: rate-distortion curve prediction, per-title encoding profile generation, and video encoder comparison.