Optimization of Probability Distributions for Residual Coding of Screen Content

TL;DR

This paper improves residual coding in screen content compression by adaptively trimming probability distributions and enhancing new color modeling, achieving up to 2.9% bitrate reduction and significant gains over HEVC and FLIF.

Contribution

The paper introduces adaptive trimming of residual probability distributions and better new color modeling for the SCF method, enhancing compression efficiency.

Findings

Bitrate reduced by up to 2.9% with the new method.

Achieves about 11% savings over HEVC and 18% over FLIF.

Improved residual coding enhances overall screen content compression.

Abstract

Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9% on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11% and 18% rate, respectively.