7 May 1997 Effects of quantization and truncation strategies on image quality during lossy image compression
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Abstract
Alternative strategies used for wavelet-based lossy image compression can affect lesion detection differently at higher compression ratios. These effects were studied using three variants of a wavelet-based image compression algorithm: (1) unified quantization, (2) truncation of all coefficients at all subbands, and (3) truncation of coefficients subband by subband. The nonprewhitening- matched-filter-derived da, a deductibility index, was used to quantify the changes in detection performance as a function of compression ratio for each strategy. Based on this approach, the optimal compression strategy was determined. Two classes of images were generated to simulate signal-present and signal-absent cases for a liver imaged by CT. For each strategy, the performance in discriminating between the signal-present class and signal-absent class was quantified by da for varying compression ratios. Among the three strategies studied, truncation of all coefficients is the least desirable strategy for preserving small, low contrast signals; truncation of coefficients subband by subband yields the best result for subtle signals, but distorts high frequency edges between tissues; unified quantization is the best strategy if both low contrast objects and high frequency edges are to be preserved.
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Binsheng Zhao, Peter Klaus Kijewski, "Effects of quantization and truncation strategies on image quality during lossy image compression", Proc. SPIE 3031, Medical Imaging 1997: Image Display, (7 May 1997); doi: 10.1117/12.273961; https://doi.org/10.1117/12.273961
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