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Related Experiment Videos

Adaptive transforms for image coding using spatially varying wavelet packets.

K Ramchandran¹, Z Xiong, K Asai

¹Illinois Univ., Urbana, IL.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1996

Summary

This summary is machine-generated.

This study introduces an adaptive image representation using spatially varying wavelet packets (WPs) for improved lossy image compression. The novel method jointly optimizes spatial and frequency decompositions for enhanced coding performance.

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Area of Science:

Digital Image Processing
Signal Processing
Computer Vision

Background:

Traditional image compression methods often use fixed transforms.
Wavelet packets (WPs) offer more flexibility in frequency decomposition.
Optimizing both spatial and frequency domains simultaneously is computationally challenging.

Purpose of the Study:

To develop a novel adaptive image representation for lossy image compression.
To jointly optimize spatial segmentation and wavelet packet frequency decomposition.
To enhance the rate-distortion performance in image coding.

Main Methods:

Utilized a fast double-tree algorithm to optimize a rate-distortion cost function.
Employed quadtree spatial segmentations and binary WP frequency decompositions.
Integrated spatial and frequency segmentation for optimal representation.

Main Results:

Demonstrated the usefulness and versatility of the adaptive representation.
Achieved improved performance with both entropy-based and SPQ-based wavelet coders.
Verified the effectiveness of joint spatial and frequency optimization.

Conclusions:

The proposed adaptive image representation significantly enhances lossy image compression.
Joint optimization of spatial and frequency domains is crucial for optimal performance.
This approach offers a versatile framework for advanced image coding applications.