Quantized and Regularized Optimization for Coding Images Using Steered Mixtures-of-Experts

Abstract

Compression algorithms that employ Mixtures-of-Experts depart drastically from standard hybrid block-based transform domain approaches as in JPEG and MPEG coders. In previous works we introduced the concept of Steered Mixtures-of-Experts (SMoEs) to arrive at sparse representations of signals. SMoEs are gating networks trained in a machine learning approach that allow individual experts to explain and harvest directional long-range correlation in the N-dimensional signal space. Previous results showed excellent potential for compression of images and videos but the reconstruction quality was mainly limited to low and medium image quality. In this paper we provide evidence that SMoEs can compete with JPEG2000 at mid-and high-range bit-rates. To this end we introduce a SMoE approach for compression of color images with specialized gates and steering experts. A novel machine learning approach is introduced that optimizes RD-performance of quantized SMoEs towards SSIM using fake quantization. We drastically improve our previous results and outperform JPEG by up to 42%.