Paper
22 April 1996 Visual thresholds for wavelet quantization error
Andrew B. Watson, Gloria Y. Yang, Joshua A. Solomon, John D. Villasenor
Author Affiliations +
Proceedings Volume 2657, Human Vision and Electronic Imaging; (1996) https://doi.org/10.1117/12.238735
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
Abstract
The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter, which we call DWT uniform quantization noise. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2-L, where r is display visual resolution in pixels/degree, and L is the wavelet level. Amplitude thresholds increase rapidly with spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from low-pass to horizontal/vertical to diagonal. We propose a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a 'perceptually lossless' quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrew B. Watson, Gloria Y. Yang, Joshua A. Solomon, and John D. Villasenor "Visual thresholds for wavelet quantization error", Proc. SPIE 2657, Human Vision and Electronic Imaging, (22 April 1996); https://doi.org/10.1117/12.238735
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Cited by 72 scholarly publications and 2 patents.
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KEYWORDS
Discrete wavelet transforms

Quantization

Visualization

Optical resolution

Spatial frequencies

Wavelets

Image compression

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