| Paper: | IMDSP-L3.5 | ||
| Session: | Still Image Coding | ||
| Time: | Thursday, May 20, 10:50 - 11:10 | ||
| Presentation: | Lecture | ||
| Topic: | Image and Multidimensional Signal Processing: Image and Video Coding | ||
| Title: | SCALABLE IMAGE EMBEDDINGS FROM ARBITRARY WAVELET-BASED PERCEPTUAL MODELS | ||
| Authors: | Matthew Gaubatz; Cornell University | ||
| Sheila Hemami; Cornell University | |||
| Abstract: | Embedded image coding is a compression technique that yields bit streams robust for transmission. The first embedded wavelet-based compression algorithms were designed to optimize for mean-squared-error (MSE). More recent algorithms incorporate a variety of perceptually-based optimization criteria, which are implemented by selecting subband quantization step-sizes based on a target quality (and therefore rate). Since these step-sizes vary with rate, a truncated bit stream optimized for a specific rate will not necessarily reflect the perceptual quality achievable at this truncated rate. Constraints are given under which a wavelet-based perceptual model can generate an embedded image representation yielding perceptually derived results over a range of rates. This kind of embedding is implemented by strategically selecting the order in which subband bit planes are coded. When truncated, the resulting embedded streams provide perceptually tuned images. | ||
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