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Technical Program
Paper Detail
| Paper: | TP-P2.10 |
| Session: | Wavelets and Scalable Video Coding |
| Time: | Tuesday, October 10, 14:20 - 17:00 |
| Presentation: |
Poster
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| Title: |
SPATIALLY-ADAPTIVE WAVELET IMAGE COMPRESSION VIA STRUCTURAL MASKING |
| Authors: |
Matthew Gaubatz; Cornell University | | | | | Stephanie Kwan; Cornell University | | | | | Bobbie Chern; Cornell University | | | | | Damon Chandler; Cornell University | | | | | Sheila Hemami; Cornell University | | |
| Abstract: |
Wavelet-based spatial quantization is a technique to compress image data that adapts the compression to the data in each region of an image. This approach is motivated because quantization with a single step-size does not result in a uniform visual effect across each spatial location; different types of image content mask quantization errors in different ways. While many spatial quantization techniques determine step-sizes via local activity measures, the proposed method induces local quantization distortion based on experiments that quantify human detection of this distortion as function of both the contrast and the type of the image data. Three types in particular, textures, structures and edges, are considered. A classifier is utilized to detect to which of these three categories a local region of image data belongs, and step-sizes are then derived based on the contrast and class of each region. Class and contrast data are conveyed to the coder with explicit side information. For images compressed at threshold, the proposed method requires 3-10 % less rate than a similar previous approach without classification, and on average produces images that are preferred by 2/3 of tested viewers. |
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