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\ell_{2} Optimized Predictive Image Coding With \ell_{\infty} Bound


Abstract:

In many scientific, medical, and defense applications of image/video compression, an l∞ error bound is required. However, pure l∞-optimized image coding, colloquially kno...Show More

Abstract:

In many scientific, medical, and defense applications of image/video compression, an l error bound is required. However, pure l-optimized image coding, colloquially known as near-lossless image coding, is prone to structured errors such as contours and speckles if the bit rate is not sufficiently high; moreover, most of the previous l-based image coding methods suffer from poor rate control. In contrast, the l2 error metric aims for average fidelity and hence preserves the subtlety of smooth waveforms better than the l∞ error metric and it offers fine granularity in rate control, but pure l2-based image coding methods (e.g., JPEG 2000) cannot bound individual errors as the l-based methods can. This paper presents a new compression approach to retain the benefits and circumvent the pitfalls of the two error metrics. A common approach of near-lossless image coding is to embed into a DPCM prediction loop a uniform scalar quantizer of residual errors. The said uniform scalar quantizer is replaced, in the proposed new approach, by a set of context-based l2-optimized quantizers. The optimization criterion is to minimize a weighted sum of the l2 distortion and the entropy while maintaining a strict l error bound. The resulting method obtains good rate-distortion performance in both l2 and l metrics and also increases the rate granularity. Compared with JPEG 2000, the new method not only guarantees lower l error for all bit rates, but also it achieves higher PSNR for relatively high bit rates.
Published in: IEEE Transactions on Image Processing ( Volume: 22, Issue: 12, December 2013)
Page(s): 5271 - 5281
Date of Publication: 18 October 2013

ISSN Information:

PubMed ID: 24144660

References

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