The Role of Phase in Adaptive Image Coding
UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES IMAGE PROCESSING INST
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The report includes a detailed analysis of image modeling aspects of the transform coding problem. Two alternate prediction algorithms are analyzed for the transform sample variance estimation the first technique uses a two- dimensional polynomial to model the image power spectral density the second technique is a simple recursive approach based on previously quantized values. The generalized phase concept is developed and plays a vital role in the coding algorithms. Both the Fourier and Walsh transforms are used, the former being demonstrated to have superior performance. A non-negative image constraint is explored via the Lukosz bound. The experimental phase of the study includes two dimensional coding of monochrome, and three dimensional coding of color, as well as interframe images with coding at 0.38, 0.55, and 0.25 bitspixel, respectively. It is demonstrated that adaptive transform domain modeling is important, and that large-size transforms, in conjunction with the proper image model, can significantly outperform block-encoding techniques.