University of Waterloo
200 University Ave W, Waterloo, ON
N2L 3G1
Phone: (519) 888-4567
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Jeffrey Brian Erbrecht
Context Adaptive Space Quantization for Image Coding
En-Hui Yang
JPEG, one of the most widely used lossy image coders in the world, has a significant limitation: only one quantization table can be used per colour channel. In this seminar, we describe a new kind of quantization method called context adaptive space quantization (CASQ), as well as a new image coding scheme based on CASQ. This image coding scheme partitions an image into homogeneous and non-homogeneous regions, or contexts; subsequent quantization is then conditioned on these contexts, achieving bit rate reduction while simultaneously preserving more information from the perceptually important parts of the image. We formulate CASQ in both hard-decision quantization (HDQ) and soft-decision quantization (SDQ) settings. For HDQ with JPEG-style Huffman coding, CASQ yields an average gain of 1.78~dB PSNR performance compared to the standard JPEG quantization table, and 0.23~dB compared to state-of-the-art HDQ methods. Using adaptive runlength coding instead of Huffman, these gains are even higher. Significant gains are also observed in the SDQ setting. All of this comes at a very low increase in computational complexity.
University of Waterloo
200 University Ave W, Waterloo, ON
N2L 3G1
Phone: (519) 888-4567
Staff and Faculty Directory
Contact the Department of Electrical and Computer Engineering
The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land promised to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is centralized within our Indigenous Initiatives Office.