Low-complexity lossless and fine-granularity scalable near-lossless compression of color images

Abstract

Summary form only given. We present a method that extends lossless compression with the feature of fine-granularity scalable near lossless compression while preserving the high compression efficiency and low complexity exhibited by dedicated lossless compression methods when compared to the scalable compression methods developed for lossy image compression. The method operates by splitting the image pixel values into their most significant bits (MSB) and least significant bits (LSB). The MSB are losslessly compressed by a dedicated lossless compression method (e.g. JPEG-LS). The LSB are compressed by a scalable encoder, i.e. in such a way that their description may be truncated at any desired point. We also present a method to automatically and adaptively determine the MSB/LSB split point such that a scalable bit string is obtained without affecting the compression efficiency and without producing compression artefacts for near-lossless compression. To determine the split point, first a low complexity DPCM-type prediction is carried out on the original pixel values to obtain the prediction error signal. Next, the split point is computed from the average value of the magnitude of this signal. Finally, applying a (lossless) color transform to decorrelate the image color components before compressing them provides a higher (lossless) compression ratio.