Closed-form quality measures for compressed medical images: compression noise statistics of transform coding

Dunling Li; Murray H. Loew

doi:10.1117/12.536124

4 May 2004 Closed-form quality measures for compressed medical images: compression noise statistics of transform coding

Dunling Li, Murray H. Loew

Proceedings Volume 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment; (2004) https://doi.org/10.1117/12.536124
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

This paper provides a theoretical foundation for the closed-form expression of model observers on compressed images. In medical applications, model observers, especially the channelized Hotelling observer, have been successfully used to predict human observer performance and to evaluate image quality for detection tasks in various backgrounds. To use model observers, however, requires knowledge of noise statistics. This paper first identifies quantization noise as the sole distortion source in transform coding, one of the most commonly used methods for image compression. Then, it represents transform coding as a 1-D block-based matrix expression, it further derives first and second moments, and the probability density function (pdf) of the compression noise at pixel, block and image levels. The compression noise statistics depend on the transform matrix and the quantization matrix in the transform coding algorithm. Compression noise is jointly normally distributed when the dimension of the transform (the block size) is typical and the contents of image sets vary randomly. Moreover, this paper uses JPEG as a test example to verify the derived statistics. The test simulation results show that the closed-form expression of JPEG quantization and compression noise statistics correctly predicts the estimated ones from actual images.

Citation Download Citation

Dunling Li and Murray H. Loew "Closed-form quality measures for compressed medical images: compression noise statistics of transform coding", Proc. SPIE 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment, (4 May 2004); https://doi.org/10.1117/12.536124

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KEYWORDS

Bismuth

Image compression

Quantization

Statistical analysis

Medical imaging

Image quality

Quality measurement

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