MSE and PSNR prediction for ADCT coder applied to lossy image compression

Abstract : Lossy compression of images is used for numerous applications nowadays. Typical requirements to it are to provide a higher compression ratio (CR) for a desired quality and to perform this quickly enough. Advanced discrete cosine transform (ADCT) based coder potentially provides a good compromise between CR and image quality but consumes essential resources for reaching a desired quality of compressed data. To get around this shortcoming, we develop a fast and rather accurate approach to prediction of mean square error (MSE) or peak signal-to-noise ratio (PSNR). This approach performs sufficiently faster than compression and allows saving time and resources. It is shown that if an image to be compressed is corrupted by noise, prediction correction is possible and desirable. © 2018 IEEE.
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Conference papers
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01874625
Contributor : Laurent Jonchère <>
Submitted on : Friday, September 14, 2018 - 2:50:16 PM
Last modification on : Friday, August 9, 2019 - 2:26:02 PM

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S. Krivenko, M. Zriakhov, V. Lukin, B. Vozel. MSE and PSNR prediction for ADCT coder applied to lossy image compression. 9th IEEE International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, May 2018, Kyiv, Ukraine. pp.613-618, ⟨10.1109/DESSERT.2018.8409205⟩. ⟨hal-01874625⟩

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