Compression for Multiple Reconstructions

TL;DR

This paper introduces a method to optimize lossy image compression for networks with diverse display systems, ensuring high-quality reconstructions across different devices by adapting standard compression techniques.

Contribution

It presents a novel rate-distortion optimization framework that accounts for multiple display models, integrating standard compression methods with an ADMM-based iterative solution.

Findings

Outperforms existing methods in multi-display compression scenarios.

Effective adaptation of HEVC standard for diverse display conditions.

Demonstrates improved image quality across various display models.

Abstract

In this work we propose a method for optimizing the lossy compression for a network of diverse reconstruction systems. We focus on adapting a standard image compression method to a set of candidate displays, presenting the decompressed signals to viewers. Each display is modeled as a linear operator applied after decompression, and its probability to serve a network user. We formulate a complicated operational rate-distortion optimization trading-off the network's expected mean-squared reconstruction error and the compression bit-cost. Using the alternating direction method of multipliers (ADMM) we develop an iterative procedure where the network structure is separated from the compression method, enabling the reliance on standard compression techniques. We present experimental results showing our method to be the best approach for adjusting high bit-rate image compression (using the state-of-the-art HEVC standard) to a set of displays modeled as blur degradations.