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Proceedings Paper

Color retinal image coding based on entropy-constrained vector quantization
Author(s): Agung W. Setiawan; Andriyan B. Suksmono; Tati R. Mengko
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Paper 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.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75461K (26 February 2010); doi: 10.1117/12.854099
Show Author Affiliations
Agung W. Setiawan, Institut of Teknologi Bandung (Indonesia)
Andriyan B. Suksmono, Institut of Teknologi Bandung (Indonesia)
Tati R. Mengko, Institut of Teknologi Bandung (Indonesia)

Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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