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Optimizing the Quantization Parameters of the JPEG Compressor to a High Quality of Fine-Detail Rendition

  • Representation, Processing, Analysis, and Understanding of Images
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Abstract

This paper describes a new algorithm for adaptive selection of DCT quantization parameters in the JPEG compressor. The quantization parameters are selected by classification of blocks based on the composition of fine details whose contrast exceeds the threshold visual sensitivity. Fine details are identified by an original search and recognition algorithm in the N-CIELAB normalized color space, which allows us to take visual contrast sensitivity into account. A distortion assessment metric and an optimization criterion for quantization of classified blocks to a high visual quality are proposed. A comparative analysis of test images in terms of compression parameters and quality degradation is presented. The new algorithm is experimentally shown to improve the compression of photorealistic images by 30% on average while preserving their high visual quality.

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References

  1. W. B. Pennebaker and J. L. Mitchel, JPEG Still Image Data Compression Standard (Springer, New York, 1992).

    Google Scholar 

  2. G. K. Wallace, “The JPEG still picture compression standard,” IEEE Trans. Consumer Electron. 38 (1), 1–67 (1992).

    Article  Google Scholar 

  3. F. Ernawan, and S. H. Nugraini, “The optimal quantization matrices for JPEG image compression from psychovisual threshold,” J. Theor. Appl. Inf. Techn. 70 (3), 566–572 (2014).

    Google Scholar 

  4. B. Aruna and Ch. Ramesh “A method for signaling block-adaptive quantization in baseline sequential JPEG,” Int. J. Modern Eng. Res. 2 (4), 2829–2831 (2012).

    Google Scholar 

  5. G. Gowripushpa, G. Santoshi, B. Ravikiran, J. Sharmila Rani, and K. Sri Harsha, “Implementation of ROI based baseline sequential adaptive quantization,” Int. J. Emerging Technol. Adva. Eng. 4 (2) (2014).

    Google Scholar 

  6. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice Hall, 2008).

    Google Scholar 

  7. M. D. Fairchild, Color Appearance Models (John Wiley and Sons, 2005).

    Google Scholar 

  8. X. Zhang, D. Silverstein, J. Farrell, and B. Wandell, “Color image quality metric SCIELAB and its application on halftone texture visibility,” in Proc. IEEE Compcon’ 97 (San Jose, CA, 1997), pp. 44–48.

    Chapter  Google Scholar 

  9. S. V. Sai, N. Yu. Sorokin, and A. G. Shoberg, “Segmentation of fine details in the CIELAB,” in Proc. 24th Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision. WSCG 2016 (Plzen, May 30–June 3, 2016), pp. 155–162.

    Google Scholar 

  10. S. V. Sai, “Fine-detail level of photorealistic images: application in the multimedia system,” in Proc. IEEE Int. Siberian Conf. on Control and Communications (SIBCON-2015) (Omsk, 2015).

    Google Scholar 

  11. G. Wyszecki, “Uniform color scales: CIE 1964 U*V*W* conversion of OSA committee selection,” J. Opt. Soc. Am. 65, 456–460 (1975).

    Article  Google Scholar 

  12. D. B. Judd, Color in Business, Science and Industry (John Wiley & Sons, 1975).

    Google Scholar 

  13. S. V. Sai, “Image segmentation algorithm in the system focusing digital camera,” in Proc. SPIE, Vol. 10176: Fundamental and Applied Problems of Photonics: Selected Papers of APCOM’2016 (2016).

    Google Scholar 

  14. P. G. J. Barten, Contrast Sensitivity of the Human Eye and Its Effects on Image Quality (HV Press, Knegsel, 1999).

    Book  Google Scholar 

  15. W. K. Pratt, Digital Image Processing (Wiley, 2001).

    Book  MATH  Google Scholar 

  16. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Processing 13 (4), 600–612 (2004).

    Article  Google Scholar 

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Authors and Affiliations

  1. Pacific National University, ul. Tikhookeanskaya 136, Khabarovsk, 680035, Russia

    S. V. Sai

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  1. S. V. Sai
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Correspondence to S. V. Sai.

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Sergei Vladimirovich Sai. Born 1960. Graduated from the Tomsk University of Control Systems and Radioelectronics in 1983. Received candidate’s degree in 1990 and doctoral degree in 2003. Head of the Department of Computer Science at the Pacific National University. Scientific interests: digital processing, analysis, and recognition of images. Author of 98 papers, including 3 monographs and 16 papers in journals indexed in Scopus and Web of Science.

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Sai, S.V. Optimizing the Quantization Parameters of the JPEG Compressor to a High Quality of Fine-Detail Rendition. Pattern Recognit. Image Anal. 28, 71–78 (2018). https://doi.org/10.1134/S1054661818010157

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