Reversible denoising and lifting based color component transformation for lossless image compression

Starosolski, Roman

doi:10.1007/s11042-019-08371-w

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…A promising direction of further research is the use of the detector precision characteristic (DPC) method [ 39 ], which allows for a virtually costless adaptive construction of the transform based on a model that is driven by image acquisition parameters, which are normally stored along with medical volumes. We have already obtained positive results by employing DPC to adaptively select denoising filters for RDLS-modified color space transforms [ 21 ]. Furthermore, we suspect that RDLS effects could be improved by using sophisticated denoising filters, which, in conjunction with the adaptive DPC-based method of their selection, may allow the most sophisticated hybrid transform HP+RDLS-SS-DWT+Pred to obtain greater compression ratio improvements at an acceptably low cost.…”

Section: Discussionmentioning

confidence: 99%

“…RDLS with step skipping was successfully applied to reversible color space transforms [ 19 , 21 ] (to standard RCT [ 5 ], standard YCoCg-R [ 22 ], and to two simpler ones [ 19 , 23 ]) and to multiple-level 2D-DWT [ 9 ]. It resulted in practically useful improvements of lossless compression ratios for the reversible color space transforms (in the case of standard algorithms JPEG-LS [ 24 , 25 ], JPEG 2000, and JPEG XR [ 26 , 27 ]), and for DWT in the case of JPEG 2000 coding.…”

Section: Materials and Methodsmentioning

confidence: 99%

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Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

Starosolski

2020

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The primary purpose of the reported research was to improve the discrete wavelet transform (DWT)-based JP3D compression of volumetric medical images by applying new methods that were only previously used in the compression of two-dimensional (2D) images. Namely, we applied reversible denoising and lifting steps with step skipping to three-dimensional (3D)-DWT and constructed a hybrid transform that combined 3D-DWT with prediction. We evaluated these methods using a test-set containing images of modalities: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). They proved effective for 3D data resulting in over two times greater compression ratio improvements than competitive methods. While employing fast entropy estimation of JP3D compression ratio to reduce the cost of image-adaptive parameter selection for the new methods, we found that some MRI images had sparse histograms of intensity levels. We applied the classical histogram packing (HP) and found that, on average, it resulted in greater ratio improvements than the new sophisticated methods and that it could be combined with these new methods to further improve ratios. Finally, we proposed a few practical compression schemes that exploited HP, entropy estimation, and the new methods; on average, they improved the compression ratio by up to about 6.5% at an acceptable cost.

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Section: Discussionmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

Starosolski

2020

Entropy

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“…In ongoing research, we are investigating an application of RDLS-SS-DWT+Pred to the 3-dimensional DWT employed by JP3D in lossless compression of volumetric medical images as well as RDLS filter selection using the DPC model in the case of RDLS-modified color space transforms [ 38 ]. The effectiveness of the memoryless entropy as an estimator of coding effects that we used for adaptive selection of filters and predictors may be improved by using, instead of H 0, a conditional entropy better matching the actual JPEG 2000 context entropy coder.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform

Starosolski¹

2020

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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.

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“…In recent research, 8 Starosolski 6 revisited and discussed the effects of employing reversible denoising for reversible colour transformation in predictive lossless image compression. Similarly, a hybrid technique employing prediction stage as well as Discrete Wavelet Transform (DWT) is introduced in Starosolski 9 which is shown to produce promising results.…”

Section: Introductionmentioning

confidence: 99%

A content awareness module for predictive lossless image compression to achieve high throughput data sharing over the network storage

Rajput

Akhtar

et al. 2022

International Journal of Distributed Sensor Networks

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The idea of applying integer Reversible Colour Transform to increase compression ratios in lossless image compression is a well-established and widely used practice. Although various colour transformations have been introduced and investigated in the past two decades, the process of determining the best colour scheme in a reasonable time remains an open challenge. For instance, the overhead time (i.e. to determine a suitable colour transformation) of the traditional colour selector mechanism can take up to 50% of the actual compression time. To avoid such high overhead, usually, one pre-specified transformation is applied regardless of the nature of the image and/or correlation of the colour components. We propose a robust selection mechanism capable of reducing the overhead time to 20% of the actual compression time. It is postulated that implementing the proposed selection mechanism within the actual compression scheme such as JPEG-LS can further reduce the overhead time to 10%. In addition, the proposed scheme can also be extended to facilitate network-based compression–decompression mechanism over distributed systems.

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Reversible denoising and lifting based color component transformation for lossless image compression

Cited by 6 publications

References 25 publications

Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform

A content awareness module for predictive lossless image compression to achieve high throughput data sharing over the network storage

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