Skipping Selected Steps of DWT Computation in Lossless JPEG 2000 for Improved Bitrates

In order to improve bitrates of lossless JPEG 2000, we propose to modify the discrete wavelet transform (DWT) by skipping selected steps of its computation. We employ a heuristic to construct the skipped steps DWT (SS-DWT) in an image-adaptive way and define fixed SS-DWT variants. For a large and diverse set of images, we find that SS-DWT significantly improves bitrates of non-photographic images. From a practical standpoint, the most interesting results are obtained by applying entropy estimation of coding effects for selecting among the fixed SS-DWT variants. This way we get the compression scheme that, as opposed to the general SS-DWT case, is compliant with the JPEG 2000 part 2 standard. It provides average bitrate improvement of roughly 5% for the entire test-set, whereas the overall compression time becomes only 3% greater than that of the unmodified JPEG 2000. Bitrates of photographic and non-photographic images are improved by roughly 0.5% and 14%, respectively. At a significantly increased cost of exploiting a heuristic, selecting the steps to be skipped based on the actual bitrate instead of an estimated one, and by applying reversible denoising and lifting steps to SS-DWT, we have attained greater bitrate improvements of up to about 17.5% for non-photographic images.

[1]  Michael G. Strintzis,et al.  Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding , 2001, IEEE Trans. Image Process..

[2]  F. Dufaux,et al.  The JPEG XR image coding standard [Standards in a Nutshell] , 2009, IEEE Signal Processing Magazine.

[3]  Peter Schelkens,et al.  Wavelet based volumetric medical image compression , 2015, Signal Process. Image Commun..

[4]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Gary J. Sullivan,et al.  Overview of the High Efficiency Video Coding (HEVC) Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Tilo Strutz,et al.  Reversible Color Spaces without Increased Bit Depth and Their Adaptive Selection , 2015, IEEE Signal Processing Letters.

[7]  I. Daubechies,et al.  Factoring wavelet transforms into lifting steps , 1998 .

[8]  Richard G. Baraniuk,et al.  Nonlinear wavelet transforms for image coding via lifting , 2003, IEEE Trans. Image Process..

[9]  Tilo Strutz Multiplierless Reversible Colour Transforms and their Automatic Selection for Image Data Compression , 2013 .

[10]  S. A. Martucci,et al.  Reversible compression of HDTV images using median adaptive prediction and arithmetic coding , 1990, IEEE International Symposium on Circuits and Systems.

[11]  Savita S. Jadhav,et al.  JPEG XR an Image Coding Standard , 2012 .

[12]  Tilo Strutz Multiplierless Reversible Color Transforms and Their Automatic Selection for Image Data Compression , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Roman Starosolski Application of reversible denoising and lifting steps to DWT in lossless JPEG 2000 for improved bitrates , 2015, Signal Process. Image Commun..

[14]  Roman Starosolski Application of reversible denoising and lifting steps with step skipping to color space transforms for improved lossless compression , 2016, J. Electronic Imaging.

[15]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2013, The Kluwer international series in engineering and computer science.

[16]  Guillermo Sapiro,et al.  The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS , 2000, IEEE Trans. Image Process..