Objective and subjective evaluations of some recent image compression algorithms

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1 31st Picture Coding Symposium May 31 June 3, 2015, Cairns, Australia Objective and subjective evaluations of some recent image compression algorithms Marco Bernando, Tim Bruylants, Touradj Ebrahimi, Karel Fliegel, Philippe Hanhart, Lukas Krasula, Antonio Pinheiro, Martin Rerabek, Peter Schelkens, He Xu A contribution from Qualinet COST Action IC1003

2 Background 2 Call for information on still image coding issued by JPEG committee on February 2015 broad scope not only limited to compression efficiency New imaging modalities (more than 8- bit, HDR, ) Features (scalability, random access, ) Characteristics (complexity, latency, ) PCS 2015 Feature Event evaluation of current and future Image compression technologies This contribution only focuses on compression efficiency of conventional images Test set and bit rates based on JPEG XR evaluation core experiments carried out by JPEG committee Objective and subjective evaluations carried out by Qualinet COST Action IC1003 VUB (Belgium) UBI (Portugal) CVUT (Czech Republic) EPFL (Switzerland)

3 Test material 3 Contents: 8 (2 training + 6 test) resolutions pixels Subjective evaluations on cropped versions to fit display Stimuli: Original images Compressed/decompressed images with 10+5 codecs BPG HEVC (420,444) JPEG2000 (420,444) VP9 USC (PSNR, SSIM) Daala JPEG JPEG- XR (420, 444) WebP (420, 444) JPEG2000 UNSW 4 (6) bit rates for each test image Content dependent Closest possible to targets bike p06 training cafe p10 woman p01

4 Images used in evaluations with objective metrics p04 p14 bike cafe 31st Picture Coding Symposium, May 3 1 June 3, 2 015, Cairns, Australia 4

5 Images used in evaluations with objective metrics woman p06 p10 p01 31st Picture Coding Symposium, May 3 1 June 3, 2 015, Cairns, Australia 5

6 Cropped images used in subjective metrics training bike cafe

7 Cropped images used in subjective metrics woman p06 p10 p01 31st Picture Coding Symposium, May 3 1 June 3, 2 015, Cairns, Australia 7

8 Subjective evaluation methodology 8 ACR- HR: Absolute Category Rating with Hidden Reference 5- level discrete scale from poor to bad to fair to good to excellent Randomization of presentation order Expert viewing methodology based on ITU- R BT codecs tested for their subjective quality 6(codecs)X6(images)X4(bit rates)+6(originals)=150 stimuli 27 experts selected from QoMEX2015 participants 3 sessions of 50 stimuli (circa 15 min per session) 9 scores per stimuli Short training for bad, fair and excellent quality illustrations Display: Apple MacBook Pro Retina 15in time Training Stimulus 1 Vote 1 Stimulus 50 Vote 50

9 Objective evaluation metrics PSNR Y widely used quality metric in image processing community MSSIM: Mean Structural Similarity Index considers that the HVS uses the structural information from a scene changes in the structural information from the reference and distorted images can be perceived as a measure of distortion the implementation is a part of the Metrix Mux package FSIM: Feature Similarity Index adds a comparison of low- level features between the reference and the distorted images analyzes the high phase congruency extracting highly informative features and the gradient magnitude, to encode the contrast information this analysis is complementary and reflects different aspects of the HVS in assessing the local quality of an image More information can be found at

10 Objective evaluation metrics CIEDE2000: Color difference metric includes weighting factors for lightness, chroma, and hue (like the CIE1976 L*a*b* perceptual space). includes factors to also handle the relationship between chroma and hue. VIF: Visual Information Fidelity analyses the natural scene statistics uses an information theory based image degradation and the HVS model based on the quantification of the Shannon information present in both the reference and the distorted images the implementation is a part of the Metrix Mux package HDR- VDP2: High Dynamic Range Visible Difference Predictor calibrated metric developed for HDR images considers a light- adaptive contrast sensitivity function, as the ranges of light adaptation can vary substantially includes a specific model of the point spread function (PSF) of the eye optics, as human optical lens flare can be very strong in high contrast HDR content the front- end amplitude non- linearity is based on integration of the Weber- Fechner law takes into account the angular resolution uses a multi- scale decomposition a neural noise block is defined to calculate per- pixel probabilities maps of visibility and the predicted MOS

11 PSNR Y results 11

12 MSSIM results 12

13 VIF Y results 13

14 HDR- VDP2 results 14

15 FSIM results 15

16 CIEDE2000 results 16

17 Subjective evaluation results 17 bike cafe

18 Subjective evaluation results 18 woman p06

19 Subjective evaluation results 19 p10 p01

20 20 Thank you for your attention!

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