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Proceedings Paper

Image quality (IQ) guided multispectral image compression
Author(s): Yufeng Zheng; Genshe Chen; Zhonghai Wang; Erik Blasch
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Paper Abstract

Image compression is necessary for data transportation, which saves both transferring time and storage space. In this paper, we focus on our discussion on lossy compression. There are many standard image formats and corresponding compression algorithms, for examples, JPEG (DCT — discrete cosine transform), JPEG 2000 (DWT — discrete wavelet transform), BPG (better portable graphics) and TIFF (LZW — Lempel-Ziv-Welch). The image quality (IQ) of decompressed image will be measured by numerical metrics such as root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural Similarity (SSIM) Index. Given an image and a specified IQ, we will investigate how to select a compression method and its parameters to achieve an expected compression. Our scenario consists of 3 steps. The first step is to compress a set of interested images by varying parameters and compute their IQs for each compression method. The second step is to create several regression models per compression method after analyzing the IQ-measurement versus compression-parameter from a number of compressed images. The third step is to compress the given image with the specified IQ using the selected compression method (JPEG, JPEG2000, BPG, or TIFF) according to the regressed models. The IQ may be specified by a compression ratio (e.g., 100), then we will select the compression method of the highest IQ (SSIM, or PSNR). Or the IQ may be specified by a IQ metric (e.g., SSIM = 0.8, or PSNR = 50), then we will select the compression method of the highest compression ratio. Our experiments tested on thermal (long-wave infrared) images (in gray scales) showed very promising results.

Paper Details

Date Published: 19 May 2016
PDF: 10 pages
Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710C (19 May 2016); doi: 10.1117/12.2225532
Show Author Affiliations
Yufeng Zheng, Alcorn State Univ. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Zhonghai Wang, Intelligent Fusion Technology, Inc. (United States)
Erik Blasch, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 9871:
Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
Liyi Dai; Yufeng Zheng; Henry Chu; Anke D. Meyer-Bäse, Editor(s)

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