JPEG-based perceptual image coding with block-based image quality metric

Lina Jin, Karen O. Egiazarian, C.-C. Jay Kuo

Abstract: A JPEG-based perceptual image coder is proposed in this work, where a block-based image quality metric is used to optimize the rate-quality (RQ) performance. Under this framework, the quality of each image block is measured using a local quality metric while the overall image quality is evaluated by summing up all local quality metrics. A rate-quality optimization (RQO) problem is formulated in each macroblock of size 16×16 based on its associated empirical RQ curve. Then, to achieve the best perceptual image quality under a given bit budget constraint, the set of optimal quantization parameters (QPs) for image blocks is solved using the Lagrangian approach. It is demonstrated that the proposed perceptual image codec offers a significant improvement over the JPEG baseline in both subjective and objective evaluations.