Psychovisual and statistical optimization of quantization tables for DCT compression engines

The paper presents a new and statistically robust algorithm able to improve the performance of the standard DCT compression algorithm for both perceived quality and compression size. The approach proposed combines together an information theoretical/statistical approach with HVS (human visual system) response functions. The methodology applied permits us to obtain a suitable quantization table for specific classes of images and specific viewing conditions. The paper presents a case study where the right parameters are learned after an extensive experimental phase, for three specific classes: document, landscape and portrait. The results show both perceptive and measured (in term of PSNR) improvement. A further application shows how it is possible obtain significant improvement profiling the relative DCT error inside the pipeline of images acquired by typical digital sensors.