Color retinal image coding based on entropy-constrained vector quantization

Agung W. Setiawan; Andriyan B. Suksmono; Tati R. Mengko

doi:10.1117/12.854099

26 February 2010 Color retinal image coding based on entropy-constrained vector quantization

Agung W. Setiawan, Andriyan B. Suksmono, Tati R. Mengko

Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75461K (2010) https://doi.org/10.1117/12.854099
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore

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.

Citation Download Citation

Agung W. Setiawan, Andriyan B. Suksmono, and Tati R. Mengko "Color retinal image coding based on entropy-constrained vector quantization", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75461K (26 February 2010); https://doi.org/10.1117/12.854099

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