Scalable near-lossless image coding

In near-lossless image coding, each reconstructed pixel of the decoded image differs from the corresponding one in the original image by not more than a prespecifled value. Such schemes are mainly based on predictive coding techniques, which are not capable of quality or resolution-wise scalable decoding. Lossless image coding with scalable decoding is mainly based on transforms that map integers to integers using lifting factorization. In this work, the near-lossless quantization is incorporated into lifting to develop a wavelet-based near-lossless image coding scheme that supports scalability. The proposed technique, which performs online quantization, eliminates the inefficiencies of prequantization-based near-lossless coding and the difficulty in wavelet domain near-lossless quantizing. Two online near-lossless quantization techniques based on 1-D and 2-D transforms are presented. The algorithms outperform the prequantization-based near-lossless image coding in both bit rate and root mean square (rms) error performances, resulting in both subjectively and objectively superior performance in scalable decoding. The 2-D online scheme results in comparable performance with JPEG-LS, which is a nonscalable coding technique. Using these novel schemes enables scalable decoding of near-lossless coded images at the expense of a small increase in bit rates compared to those achieved using JPEG-LS. (c) 2006 SPIE and IS&T