24 February 2017 Improved method for predicting the peak signal-to-noise ratio quality of decoded images in fractal image coding
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Abstract
To predict the peak signal-to-noise ratio (PSNR) quality of decoded images in fractal image coding more efficiently and accurately, an improved method is proposed. After some derivations and analyses, we find that the linear correlation coefficients between coded range blocks and their respective best-matched domain blocks can determine the dynamic range of their collage errors, which can also provide the minimum and the maximum of the accumulated collage error (ACE) of uncoded range blocks. Moreover, the dynamic range of the actual percentage of accumulated collage error (APACE), APACE min to APACE max , can be determined as well. When APACE min reaches a large value, such as 90%, APACE min to APACE max will be limited in a small range and APACE can be computed approximately. Furthermore, with ACE and the approximate APACE, the ACE of all range blocks and the average collage error (ACER) can be obtained. Finally, with the logarithmic relationship between ACER and the PSNR quality of decoded images, the PSNR quality of decoded images can be predicted directly. Experiments show that compared with the previous similar method, the proposed method can predict the PSNR quality of decoded images more accurately and needs less computation time simultaneously.
© 2017 SPIE and IS&T
Qiang Wang, Sheng Bi, "Improved method for predicting the peak signal-to-noise ratio quality of decoded images in fractal image coding," Journal of Electronic Imaging 26(1), 013024 (24 February 2017). https://doi.org/10.1117/1.JEI.26.1.013024 . Submission: Received: 31 August 2016; Accepted: 7 February 2017
Received: 31 August 2016; Accepted: 7 February 2017; Published: 24 February 2017
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