Reversible Denoising and Lifting Based Color Component Transformation for Lossless Image Compression
This work addresses noise issues in lossless image compression for applications like medical imaging or archiving, but it is incremental as it builds on existing transformation methods.
The paper tackles the problem of noise contamination in reversible color space transformations for lossless image compression by proposing a reversible denoising and lifting step (RDLS) that integrates denoising filters. It results in improved compression ratios, with average improvements of 5.0-6.0% for certain image sets and up to 2.2% for standard test-sets.
An undesirable side effect of reversible color space transformation, which consists of lifting steps (LSs), is that while removing correlation it contaminates transformed components with noise from other components. Noise affects particularly adversely the compression ratios of lossless compression algorithms. To remove correlation without increasing noise, a reversible denoising and lifting step (RDLS) was proposed that integrates denoising filters into LS. Applying RDLS to color space transformation results in a new image component transformation that is perfectly reversible despite involving the inherently irreversible denoising; the first application of such a transformation is presented in this paper. For the JPEG-LS, JPEG 2000, and JPEG XR standard algorithms in lossless mode, the application of RDLS to the RDgDb color space transformation with simple denoising filters is especially effective for images in the native optical resolution of acquisition devices. It results in improving compression ratios of all those images in cases when unmodified color space transformation either improves or worsens ratios compared with the untransformed image. The average improvement is 5.0-6.0\% for two out of the three sets of such images, whereas average ratios of images from standard test-sets are improved by up to 2.2\%. For the efficient image-adaptive determination of filters for RDLS, a couple of fast entropy-based estimators of compression effects that may be used independently of the actual compression algorithm are investigated and an immediate filter selection method based on the detector precision characteristic model driven by image acquisition parameters is introduced.