JOURNAL ARTICLE

Wavelet image coding using the spherical representation

Abstract

In this paper, we introduce the "spherical representation", which provides a new adaptive framework for modeling and coding the image information in wavelet subbands. Based on this representation, a practical coding algorithm is developed. This coder uses local energy as a direct measure to differentiate between parts of the wavelet subband and to decide how to allocate the available bitrate. As local energy becomes available at finer resolutions, i.e. in smaller size windows, the coder automatically updates its decisions about how to spend the bitrate. We use a hierarchical set of variables to specify and code the local energy up to the highest resolution, i.e. the energy of individual wavelet coefficients. The overall scheme is nonredundant, meaning that the subband information is conveyed using this equivalent set of variables without the need for any side parameters. Despite its simplicity, the algorithm produces PSNR results that are competitive with the state-of-art coders in literature.

Keywords:
Wavelet Computer science Coding (social sciences) Wavelet transform Representation (politics) Algorithm Source code Cascade algorithm Set (abstract data type) Energy (signal processing) Artificial intelligence Discrete wavelet transform Pattern recognition (psychology) Mathematics Statistics

Metrics

8
Cited By
0.28
FWCI (Field Weighted Citation Impact)
8
Refs
0.61
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Data Compression Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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