JPEG XR objective and subjective evaluations
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The document discusses objective and subjective quality assessment methods for evaluating the rate-distortion performance of JPEG-XR image compression. It provides details on various objective metrics used to compare JPEG-XR to JPEG2000 and JPEG, including PSNR, SSIM, VIF, and others. It also proposes a methodology for subjective testing using a continuous quality scale to validate the results of objective metrics. Preliminary results are presented comparing different JPEG-XR implementations using the objective metrics.
![Metric 1: Maximum Pixel Deviation (L inf ) L inf R = max [abs(Im aR (x,y)-Im bR (x,y))] Considering RGB color space: where: Im a , Im b = pictures to compare L inf G = max [abs(Im aG (x,y)-Im bG (x,y))] L inf B = max [abs(Im aB (x,y)-Im bB (x,y))] (L inf [0,1])](https://image.slidesharecdn.com/epflcontrjpegjapanfeb2009-123492134414-phpapp01/85/JPEG-XR-objective-and-subjective-evaluations-11-320.jpg)
![Estimate of luminance = mean intensity: Metric 4: Mean SSIM (MSSIM) (I) Estimate of contrast = standard deviation: [1] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “ Image Quality Assessment: From Error Measurement to Structural Similarity” (2004). Estimate of picture structure: Structural information = “attributes that represent the structure of objects in the scene, independent of the average luminance and contrast”.](https://image.slidesharecdn.com/epflcontrjpegjapanfeb2009-123492134414-phpapp01/85/JPEG-XR-objective-and-subjective-evaluations-16-320.jpg)
![Metric 4: Mean SSIM (MSSIM) (III) The SSIM indexing algorithm is applied using a sliding window approach which results in a SSIM index quality map of the image. The average of the quality map is called Mean SSIM index (MSSIM) . Weighted summation of MSSIM indexes evaluated on Y’, Cb and Cr components ( Y’CbCr color space - Rec. ITU-R BT.601 ): MSSIM = w y MSSIM Y + w Cb MSSIM Cb + w Cr MSSIM Cr where: (MSSIM [0,1])](https://image.slidesharecdn.com/epflcontrjpegjapanfeb2009-123492134414-phpapp01/85/JPEG-XR-objective-and-subjective-evaluations-18-320.jpg)
![Metric 5: Visual Information Fidelity – Pixel (VIF-P) (I) [2] H. R. Sheikh, A. C. Bovik “Image Information And Visual Quality” (2004). “ Image information measure that quantifies the information that is present in the reference image and how much this reference information can be extracted from the distorted image” using statistical approach. Natural image (source) Channel (distortion) HVS HVS C F E Reference image (E) = output of a stochastic natural source that passes through HVS channel and is processed by the brain Test image (F) = output of an image distortion channel that distorts the output of the natural source before it passes through the HVS channel](https://image.slidesharecdn.com/epflcontrjpegjapanfeb2009-123492134414-phpapp01/85/JPEG-XR-objective-and-subjective-evaluations-19-320.jpg)
![Metric 5: Visual Information Fidelity – Pixel (VIF-P) (II) Natural image modeling in wavelet domain using Gaussian scale mixtures (GSMs) Information that the brain could ideally extract from reference image = mutual information between C and E: Corresponding information that could be extracted from test image = mutual information between C and F: VIF-P is a new implementation in a multi-scale pixel domain: computationally simpler than Wavelet domain version. performance slightly worse than Wavelet domain version. where: z= source model parameters. (VIF [0,1] and VIF>1 if the test image is enhanced version of the original )](https://image.slidesharecdn.com/epflcontrjpegjapanfeb2009-123492134414-phpapp01/85/JPEG-XR-objective-and-subjective-evaluations-20-320.jpg)
![Metric 6: PSNR-HVS-M (I) Block 8x8 of distorted image Block 8x8 of original image DCT of difference between pixel values Reduction by value of contrast masking MSE H calculation of the block [3] N. Ponomarenko, F. Silvestri, K. Egiazarian, M.Carli, J. Astola, and V. Lukin, “ On between-coefficient contrast masking of DCT basis functions” (2007). DCT coefficients of 8x8 pixel blocks X and Y are visually undistinguished if: E w (X-Y) < max (E m (X), E m (Y)) where E w (block) is the energy of DCT coefficients of the block weighted according to CSF and E m (block) is the masking effect of DCT coefficients of the block which depends upon E w (block) and upon the local variances.](https://image.slidesharecdn.com/epflcontrjpegjapanfeb2009-123492134414-phpapp01/85/JPEG-XR-objective-and-subjective-evaluations-21-320.jpg)
![Metric 7: DC Tune [4] A. B. Watson, A. P. Gale, J. A. Solomon, and A. J. Ahumada JR., “ DCTune: A Techinque For Visual Optimization Of DCT Quantization Matrices For Individual Images” (1994). developed as a method for optimizing JPEG image compression by computing the JPEG quantization matrices which yields a designated perceptual error model of perceptual error based upon DCT coefficients analysis, taking into account: luminance masking. contrast masking. spatial error pooling. frequency error pooling.](https://image.slidesharecdn.com/epflcontrjpegjapanfeb2009-123492134414-phpapp01/85/JPEG-XR-objective-and-subjective-evaluations-23-320.jpg)
![Double Stimulus Continuous Quality Scale (DSCQS) method [ITU-R Rec. BT.500-11] adapted to deal with the evaluation of still pictures: Proposed methodology (I) Reference Image Test Image test picture and its reference are shown at the same time. the assessor is not told about the presence of a reference picture. positions of reference and test pictures are systematically switched. test pairs related to different original contents are always alternated.](https://image.slidesharecdn.com/epflcontrjpegjapanfeb2009-123492134414-phpapp01/85/JPEG-XR-objective-and-subjective-evaluations-39-320.jpg)
![Proposed methodology (V) At least 15 subjects Subjective data processing: computation of Differential Score (DS) : DS = Score for the reference picture – Score for the test picture ANalysis Of Variance (ANOVA) to detect eventual systematic errors and scores normalization to remove them screening to detect outliers [ITU-R Rec. BT.500-11] computation of the Differential Mean Opinion Score (DMOS)](https://image.slidesharecdn.com/epflcontrjpegjapanfeb2009-123492134414-phpapp01/85/JPEG-XR-objective-and-subjective-evaluations-43-320.jpg)
