L-Infinite Predictive Coding of Depth

Abstract

The paper introduces a novel $L_\infty$-constrained compression method for depth maps. The proposed method performs depth segmentation and depth prediction in each segment, encoding the resulting information as a base layer. The depth residuals are modeled using a Two-Sided Geometric Distribution, and distortion and entropy models for the quantized residuals are derived based on such distributions. A set of optimal quantizers is determined to ensure a fix rate budget at a minimum $L_\infty$ distortion. A fixed-rate $L_\infty$ codec design performing context-based entropy coding of the quantized residuals is proposed, which is able to efficiently meet user constraints on rate or distortion. Additionally, a scalable $L_\infty$ codec extension is proposed, which enables encoding the quantized residuals in a number of enhancement layers. The experimental results show that the proposed $L_\infty$ coding approach substantially outperforms the $L_\infty$ coding extension of the state-of-the-art CALIC method.

The work in this paper has been supported by Innoviris (3DLicorneA) and FWO.