Optimal Halftoning for Network-Based Imaging

摘要

In this contribution, we introduce a multiple depth progressive representation for network-based still and moving images. A simple quantization algorithm associated with this representation provides optimal image quality. By optimum, we mean the best possible visual quality for a given volume of information under real life constraints such as physical (network load, connection speed), psychological (viewer's expectation and patience), or legal constraints (access privileges). A special variant of the algorithm, multi-depth coherent error diffusion, addresses a specific problem of temporal coherence between frames in moving images. The output produced with our algo-rithm is visually pleasant because its Fourier spectrum is close to the "blue noise".