Conference/Proceedings2019 Data Compression Conference (DCC)
Start date2019
Author(s)Rolf Jongebloed, Erik Bochinski, Lieven lange, Thomas Sikora
TitleQuantized and Regularized Optimization for Coding Images Using Steered Mixtures-of-Experts
AbstractCompression algorithms that employ Mixtures-of-Experts depart drastically from standard
hybrid block-based transform domain approaches as in JPEG and MPEG coders. In pre-
vious 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 learn-
ing 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 com-
pete 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%.
Key wordscompression, Steered Mixture-of-Experts, Image Coding, Gradient Descent