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. 2020 Jul 9;22(7):751.
doi: 10.3390/e22070751.

Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform

Roman Starosolski  1
Affiliations

Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform

Roman Starosolski. Entropy (Basel). .

Abstract

A new hybrid transform for lossless image compression exploiting a discrete wavelet transform (DWT) and prediction is the main new contribution of this paper. Simple prediction is generally considered ineffective in conjunction with DWT but we applied it to subbands of DWT modified using reversible denoising and lifting steps (RDLSs) with step skipping. The new transform was constructed in an image-adaptive way using heuristics and entropy estimation. For a large and diverse test set consisting of 499 photographic and 247 non-photographic (screen content) images, we found that RDLS with step skipping allowed effectively combining DWT with prediction. Using prediction, we nearly doubled the JPEG 2000 compression ratio improvements that could be obtained using RDLS with step skipping. Because for some images it might be better to apply prediction instead of DWT, we proposed compression schemes with various tradeoffs, which are practical contributions of this study. Compared with unmodified JPEG 2000, one scheme improved the compression ratios of photographic and non-photographic images, on average, by 1.2% and 30.9%, respectively, at the cost of increasing the compression time by 2% and introducing only minimal modifications to JPEG 2000. Greater ratio improvements, exceeding 2% and 32%, respectively, are attainable at a greater cost.

Keywords: JPEG 2000; discrete wavelet transform; entropy estimation; hybrid transform; lossless image compression; predictive coding; reversible denoising and lifting step; screen content coding; step skipping; transform coding.

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Conflict of interest statement

Patent applications have been filed for the hybrid transform and practical compression schemes proposed in this paper. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 3
Figure 3
Improvements of bitrates of individual images obtained using RDLS-SS-DWT and RDLS-SS-DWT+Pred plotted against the bitrate of unmodified JPEG 2000.
Figure 1
Figure 1
One-level 2D-DWT (a–c) and three-level 2D-DWT (d).
Figure 2
Figure 2
A noise-contaminated image (a) and its one-level DWT (b) and RDLS-DWT (c).
Figure 4
Figure 4
Use of prediction in subbands of various origins.
Figure 5
Figure 5
Average bitrate improvements obtained with and without NO-DWT+PredMED plotted against the cost of the improvement.
See this image and copyright information in PMC

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