JNCD-Based Perceptual Compression of RGB 4:4:4 Image Data

In contemporary lossy image coding applications, a desired aim is to decrease, as much as possible, bits per pixel without inducing perceptually conspicuous distortions in RGB image data. In this paper, we propose a novel color-based perceptual compression technique, named RGB-PAQ. RGB-PAQ is based on CIELAB Just Noticeable Color Difference (JNCD) and Human Visual System (HVS) spectral sensitivity. We utilize CIELAB JNCD and HVS spectral sensitivity modeling to separately adjust quantization levels at the Coding Block (CB) level. In essence, our method is designed to capitalize on the inability of the HVS to perceptually differentiate photons in very similar wavelength bands. In terms of application, the proposed technique can be used with RGB (4:4:4) image data of various bit depths and spatial resolutions including, for example, true color and deep color images in HD and Ultra HD resolutions. In the evaluations, we compare RGB-PAQ with a set of anchor methods; namely, HEVC, JPEG, JPEG 2000 and Google WebP. Compared with HEVC HM RExt, RGB-PAQ achieves up to 77.8% bits reductions. The subjective evaluations confirm that the compression artifacts induced by RGB-PAQ proved to be either imperceptible (MOS = 5) or near-imperceptible (MOS = 4) in the vast majority of cases.