Abstract :
A Progressive Lossless/Near-Lossless Image-Compression Algorithm We present a compression technique that provides progressive transmission as well as lossless and near-lossless compression in a single framework. The proposed technique produces a bit stream that results in a progressive, and ultimately lossless, reconstruction of an image, similar to what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides near-lossless reconstruction with respect to a given bound, after decoding each layer of the successively refinable bit stream. We formulate the image data compression problem as one of successively refining the probability density function (pdf) estimate of each pixel. Within this framework, restricting the region of support of the estimated pdf to a fixed size interval then results in near-lossless reconstruction. We address the context-selection problem as well as pdf-estimation methods based on context data at any passs. Experimental results for both lossless and near-lossless cases indicate that the proposed compression scheme, that innovatively combines lossless, near-lossless, and progressive coding attributes, give competitive performance in comparison with state-of-the-art compression schemes.
