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A Comparison of Quality Metrics for JPEG Images

A Comparison of Quality Metrics for JPEG Images. Feng Xiao Fall 2000. Motivation. Compare performance of different image metrics for JPEG images with subjective measurement Blocking is the dominant artifact in JPEG images (or other block-based coding), especially at low-bit-rate

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A Comparison of Quality Metrics for JPEG Images

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  1. A Comparison of Quality Metrics for JPEG Images Feng Xiao Fall 2000 EE368B

  2. Motivation • Compare performance of different image metrics for JPEG images with subjective measurement • Blocking is the dominant artifact in JPEG images (or other block-based coding), especially at low-bit-rate • Post-processing may incur blurring when reducing blocking • Need a good metrics EE368B

  3. Candidate Metrics • RMSE (root-mean-square error) • BMR (block-to-mask ratio, Liu 1997) • EOBD (effect-of-block-distortion, Eskicioglu 1995) • MIX (RMSE + BMR) • RMSE is pixel-based, and BMR is block-based, combination may be more robust EE368B

  4. BMR: I • Compute the block difference 6 7 8 9 1 Block Border 2 EE368B

  5. BMR: II • Include the perceptual effects where is the just-noticeable difference 50 is a weighted ratio EE368B

  6. BMR: III • Separate the blocking and blurring measure • OBMR(i,j): BMR in the original image • PBMR(i,j): BMR in the processed image. • a) PBMR(i,j) > OBMR(i,j). Block(i,j) in processed image is more blocking than that of the original image. • b) PBMR(i,j) <= OBMR(i,j). Block(i,j) is blurred in processed image. • blocking strength = mean(|OBMR(i,j)-PBMR(i,j)|) for set a • blurring strength = mean(|OBMR(i,j)-PBMR(i,j)|) for set b EE368B

  7. BMR: IV • Construct the single BMR BMR= blocking strength + blurring strength EE368B

  8. BMR: V Strength Strength JPEG quality Size of smoothing filter EE368B

  9. EOBD EE368B

  10. Experiments Click on the image with the worst quality JPEG with de-block JPEG JPEG with Filtering (3x3) EE368B

  11. Experiments (cont.) • Each experiment has18x3 images: • 18 JPEG images at quality levels 5~40 (bits .25~.80 bpp) • 18 smoothed (3x3) JPEG images • 18 de-blocked JPEG images (Chou’s 1995) • Repeat 4 times • 2 subjects, 2 image sets (‘lena’ & ‘einstein’) EE368B

  12. Mean Rank Error RMSE BMR MIX EOBD Results: Comparison Rank Error for Image i: Ei= | Si – Ri |, where Si is the subjective rank of image I, Ri is the rank derived from metrics EE368B

  13. Results: Post-processing Improvement (rank order) Bit Rate (bpp) EE368B

  14. Results: RMSE vs. Subjective RMSE Subjective Rank Order EE368B

  15. Results: BMR vs. Subjective BMR Subjective Rank Order EE368B

  16. Results: EOBD vs. Subjective EOBD Subjective Rank Order EE368B

  17. Results: MIX vs. Subjective MIX Subjective Rank Order EE368B

  18. Conclusion • MIX is the best metrics as tested • It takes both pixel-based metrics (RMSE) and block-based metrics (BMR) into consideration. • Both smooth (3x3) and de-block (chou’s) show improvement for low bit-rate. EE368B

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