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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.

Summary form only given. Several methods for L/sub /spl infin//-distortion constrained compression have been proposed that target a set of fixed reconstruction-error bounds. Recently, a wavelet-based L/sub /spl infin//-constrained embedded image-coding technique was proposed that guarantees the required distortion bound while retaining the coding performance and scalability option state-of-the-art wavelet-based L/sub /spl infin//-oriented codecs. The optimality of embedded deadzone scalar-quantizers for high-rate L/sub /spl infin//-constrained scalable wavelet-based coding of images was analyzed. The optimal quantizers were employed in a coding algorithm that outperforms embedded L/sub /spl infin//-oriented wavelet-coders in terms of maximum absolute error. The algorithm was briefly described and coding results were provided.