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.
Highlights
Due to large and ever-growing sizes and quantities of images produced in the present day, compression is crucial for picture archiving and communication systems (PACSs)
It is believed that the use of prediction that is not supported by additional knowledge does not improve substantially the lossless compression ratios of discrete wavelet transform (DWT) subbands [15]
It is believed that the use of prediction that is not supported by additional knowledge does not significantly improve the lossless compression ratios of DWT subbands
Summary
Due to large and ever-growing sizes and quantities of images produced in the present day, compression is crucial for picture archiving and communication systems (PACSs). Compression ratios (bitrates) achieved by lossless image compression algorithms are not as good as those achieved by lossy algorithms, they allow for multifold increases in transmission media bandwidths and mass storage capacities of PACSs. For the above reasons, and in response to practical demands, many algorithms have been developed and adopted as international standards. JPEG 2000 is the most widely known such standard for lossy and lossless compression of image and volumetric data [3,4,5]. It is utilized, among others, in medicine and it is included in the DICOM standard [6]
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