Method for low-light-level image compression based on wavelet transform

Shaoyuan Sun; Baomin Zhang; Liping Wang; Lianfa Bai

doi:10.1117/12.445262

18 October 2001 Method for low-light-level image compression based on wavelet transform

Shaoyuan Sun, Baomin Zhang, Liping Wang, Lianfa Bai

Author Affiliations +

Proceedings Volume 4586, Wireless and Mobile Communications; (2001) https://doi.org/10.1117/12.445262
Event: Asia-Pacific Optical and Wireless Communications Conference and Exhibit, 2001, Beijing, China

Abstract

Low light level (LLL) image communication has received more and more attentions in the night vision field along with the advance of the importance of image communication. LLL image compression technique is the key of LLL image wireless transmission. LLL image, which is different from the common visible light image, has its special characteristics. As still image compression, we propose in this paper a wavelet-based image compression algorithm suitable for LLL image. Because the information in the LLL image is significant, near lossless data compression is required. The LLL image is compressed based on improved EZW (Embedded Zerotree Wavelet) algorithm. We encode the lowest frequency subband data using DPCM (Differential Pulse Code Modulation). All the information in the lowest frequency is kept. Considering the HVS (Human Visual System) characteristics and the LLL image characteristics, we detect the edge contour in the high frequency subband image first using templet and then encode the high frequency subband data using EZW algorithm. And two guiding matrix is set to avoid redundant scanning and replicate encoding of significant wavelet coefficients in the above coding. The experiment results show that the decoded image quality is good and the encoding time is shorter than that of the original EZW algorithm.

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Shaoyuan Sun, Baomin Zhang, Liping Wang, and Lianfa Bai "Method for low-light-level image compression based on wavelet transform", Proc. SPIE 4586, Wireless and Mobile Communications, (18 October 2001); https://doi.org/10.1117/12.445262

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KEYWORDS

Image compression

Computer programming

Wavelets

Wavelet transforms

Quantization

Edge detection

Image quality

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