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Curved wavelet transform for image coding
Demin Wang1, Liang Zhang, André Vincent
1Communications Research Centre Canada, Ottawa, ON K2H 8S2 Canada. demin.wang@crc.ca
Summary
A new curved wavelet transform improves image coding by applying filters along image features, outperforming JPEG2000 for edge-rich images. This method offers significant peak signal-to-noise ratio gains.
Area of Science:
- Image processing and compression
- Signal processing
- Computer vision
Background:
- Conventional 2D wavelet transforms use 1D filtering along horizontal/vertical axes, inefficiently representing image edges and lines.
- Existing image coding methods struggle with efficient representation of image features like edges and lines.
Purpose of the Study:
- To introduce a novel curved wavelet transform for enhanced image coding.
- To develop a new image coder based on the curved wavelet transform.
- To evaluate the performance of the new image coder against JPEG2000.
Main Methods:
- Developed a curved wavelet transform applying 1D filters along content-determined curves, parallel to image edges.
- Integrated the curved wavelet transform into a new image coder with JPEG2000-compatible syntax.
- Conducted image coding experiments and subjective quality assessments.
Main Results:
- The new image coder demonstrates performance equal to or better than JPEG2000.
- The curved wavelet transform is particularly effective for images with sharp edges.
- Achieved up to 1.67 dB peak signal-to-noise ratio (PSNR) gain for natural images compared to JPEG2000.
Conclusions:
- The curved wavelet transform offers a more efficient approach to image coding, especially for images with distinct edges.
- The novel image coder provides competitive or superior performance to JPEG2000.
- This transform enables better representation of image features, leading to improved compression efficiency.
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