HVS-Based Perceptual Color Compression of Image Data
Lee Prangnell, Victor Sanchez

TL;DR
This paper introduces a novel perceptual image coding method based on Human Visual System spectral sensitivity and color difference models, achieving significant data compression while maintaining high perceptual quality.
Contribution
The paper presents a new perceptual color compression technique leveraging HVS spectral sensitivity and CIELAB JNCD, improving compression efficiency without perceptual quality loss.
Findings
52.6% average BPP reduction compared to VVC
Achieves SSIM=0.99 and MS-SSIM=0.99 in tests
75% of subjective assessments rated as MOS=5
Abstract
In perceptual image coding applications, the main objective is to decrease, as much as possible, Bits Per Pixel (BPP) while avoiding noticeable distortions in the reconstructed image. In this paper, we propose a novel perceptual image coding technique, named Perceptual Color Compression (PCC). PCC is based on a novel model related to Human Visual System (HVS) spectral sensitivity and CIELAB Just Noticeable Color Difference (JNCD). We utilize this modeling to capitalize on the inability of the HVS to perceptually differentiate photons in very similar wavelength bands (e.g., distinguishing very similar shades of a particular color or different colors that look similar). The proposed PCC technique can be used with RGB (4:4:4) image data of various bit depths and spatial resolutions. In the evaluations, we compare the proposed PCC technique with a set of reference methods including…
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