Color retinal image coding based on entropy-constrained vector quantization

Abstract

Retinal color images play an important role in supporting medical diagnosis. Digital retinal image usually are represented in such a large data volume that takes a considerable amount of time to be accessed and displayed from remote site. This paper aims to conduct a color retinal image coding using Entropy-Constrained Vector Quantization (ECVQ). In this paper, we use two objective parameters: Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Coded image which has the best quality of subjective and objective is the image coded with the value of λ = 0.1 and rate = 4.5 bpp.