Visual Communications for Heterogeneous Networks/Visually Optimized Scalable Image Compression. Final Report for September 1, 1995 - February 28, 2002

Abstract

The authors developed image and video compression algorithms that provide scalability, reconstructibility, and network adaptivity, and developed compression and quantization strategies that are visually optimal at all bit rates. The goal of this research is to enable reliable ''universal access'' to visual communications over the National Information Infrastructure (NII). All users, regardless of their individual network connection bandwidths, qualities-of-service, or terminal capabilities, should have the ability to access still images, video clips, and multimedia information services, and to use interactive visual communications services. To do so requires special capabilities for image and video compression algorithms: scalability, reconstructibility, and network adaptivity. Scalability allows an information service to provide visual information at many rates, without requiring additional compression or storage after the stream has been compressed the first time. Reconstructibility allows reliable visual communications over an imperfect network. Network adaptivity permits real-time modification of compression parameters to adjust to changing network conditions. Furthermore, to optimize the efficiency of the compression algorithms, they should be visually optimal, where each bit expended reduces the visual distortion. Visual optimality is achieved through first extensive experimentation to quantify human sensitivity to supra-threshold compression artifacts and then incorporation of these experimental results into quantization strategies and compression algorithms.