Visual Processing Driven by Perceptual Quality Gauge: A Perspective Weisi Lin, Zhongkang Lu, Susanto Rahardja, EePing Ong and Susu Yao

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1 Visual Processing Driven by Perceptual Quality Gauge: A Perspective Weisi Lin, Zhongkang Lu, Susanto Rahardja, EePing Ong and Susu Yao Media Processing Department Institute for Infocomm Research, Singapore

2 Outline of Presentation Review on perceptual visual quality gauges perceptual image/video processing Some of our recent research attempts visual quality evaluation perceptual signal maniputions Concluding remarks

3 Facts about Visual Quality Evaluation as a standalone metric: image evaluation algorithm benchmarking as an embedded module: shaping algorithms/systems The HVS: ultimate appreciator of most images PSNR/MSE/MAE: not matching the HVS perception Perceptual metrics so far: much research interest (VQEG, IEEE G-2.1.6, many others) a difficult odyssey existence of general solution?

4 Different factors for perceptual metric building sensory perceptual emotional domain-specific PSNR/SNR/MSE/MAE perceptual metrics application-specific perceptual metrics performance, difficulty, complexity application scopes

5 Criterion Metric type Remarks A Glimpse on Different Metrics input signal output image video 3-D views distortion relatively well explored; color difference to be probed further simple temporal effect; pooling over frames; further modeling needed new area for single, multiple or overall distortion quality overall quality source natural scene Majority of work on natural pictures computer graphics viewer first-party most interested: third-party ones second-party third-party methodology top-down general, complex bottom-up relatively efficient reference full-reference more info available reduced-reference feature selection no-reference PSNR not applicable; wider scopes codec JPEG knowledge of artifacts to be incorporated JPEG 2000 MPEG 1,2 MPEG 4 H.261/3 H.264 application SDTV HDTV mobile comm domain knowledge to be used; piece-wise formulation for different quality ranges; PSNR largely irrelevant for mobile comm.; medical

6 Top-down Metrics single channel approach CSF filtering Mannos & Sakrison 74 Fauger 79 Lukas & Buddrikis 82 HeegerLambrecht 99 multi-channel decomposition Daly 93, Lubin 95 Lambrecht 96 Winkler 99 Watson 01 No-reference Metrics Wu & Yuen 97 Caviedes & Gurbus 02 Marziliano, et al. 02 Caviedes & Oberti 03 Dijk, et al. 03 Reduced-reference Metrics Wolf 97 Horita, et al. 03 A closer look Hybrid (top-down & bottom-up) metrics Yu, et al. 02 Ong, et al. 04 Tan & Ghanbari 00 Full-reference Metrics Daly 93 Lubin 95 Lambrecht 96 Miyahara, et al. 98 Zhang & Wandell 98 Wang, et al. 99, 04 Tan & Ghanbari 00 Winkler 99 Watson 01 Yu, et al. 02 Lin, et al. 05 Bottom-up Metrics lumonance/color difference Miyahara 98 Zhang & Wandell 98 sharpness Caviedes & Gurbus 02 Winkler 01 Dijk, et al. 03 common coding artifacts Wu & Yuen 97 Yu, et al. 02 Marziliano, et al. 02 Tan & Ghanbari 00 Mylene 03 Caviedes & Oberti 03 other features Suresh & Jayant 05 Lu, et al. 05

7 Perception-driven Visual Processing image/video compression quantizer and rate control Watson 93 Hontsch & Karam 00,02 Yang, et al. 05 foveation-based coding Wang & Bovik 01 Wang, et al. 03 Itti 04 motion search Malo, et al. 01 Yang, et al. 03 inter-frame replenishment Chiu & Berger 99 filtering of residues/coefficients Safranek 94 Yang, et al. 05 scalability Wang, et al. 03 Lu, et al. 05 closed-lopp control Tan, et al. 04 visual communication enhancement/ reconstruction watermarking Wolfgang, et al. 99 self-embedment for error correction unequal error protection Jiang, et al. 99 joint source-channel coding demoaicing Longere, et al. 02 synthesis Ramasubramanian, et al. 99 super-resolution formation post-processing Yao, et al. 05 edge-enhancement Lin, et al. 05

8 Just-noticeable Difference (JND) JND: the visibility threshold below which any change cannot be detected by the HVS (Jayant, et al. 93) differentiation in quality evaluation near-jnd supra-jnd 2 JNDs, 3 JNDs, can be also determined

9 DCT subbands Ahumada & Peterson 92, Watson 93, Hontsch & Karam 00,02, Zhang, et al. 05 wavelet subbands Watson, et al. 93 pyramid subbands Ramasubramanian, et al. 99 pixel domain Chou & Li 95, Chiu &Berger 99, Yang, et al. 03 contrast masking Tong&Venetsanopoulos 98, Zhang, et al. 05 temporal effect eye motion: Daly 98 frame difference: Chou & Chen 96 temporal CSF:for subbands-- Daly 98, Watson, et al. 01 for pixel-- Zhang 04 Visual-attention modulation Lu, et al. 05

10 Recent research attempts Visual Quality Gauge new ideas: noticeable edge contrast increase--enhancement noticeable edge contrast decrease--the worst degradation noticeable non-edge contrast decrease--degradation noticeable non-edge contrast increase degradation D + = α1c ne + α 2c ne + α 3c e α 4 c + e where α 3 > max( α 1, α 2 ) > α 4 >0 D reduces to the mean absolute error (MAE) measure, if JND is not considered different contrast changes are not differentiated

11 Recent research attempts to tell a good picture from a good one Better quality than the original image (Longere, et al. 02) (our method)

12 Recent research attempts Tests with VQEG-I Data Pearson & Spearman correlations (P0,1,3,5,8: the five best VQEG-I proponents) 95% CI the new metric: outperforms the relevant existing metrics with both databases: VQEG-I (compressed video) Longere, et al. 02 (demosaiced images) has small variation in performance under different test conditions std for all 9 test groups

13 Recent research attempts Perceptual Signal Modification 1-D illustration MC Residue (x10-1 ) modification of signal: for better compression MC Residue (x10-1 ) original signal Simplest but meaningless modification Reasonable modification: the mean in the neighborhood, B Pixel Pixel Problem: noticeable distortion introduced

14 Recent research attempts MC Residue (x10-1 ) JND range MC Residue (x10-1 ) Noticeable distortion Pixel making the distortion unnoticeable

15 Recent research attempts Perceptual Quality Significance Map (PQSM) The HVS: not with a ideal sensor with limited source -processing power -internal memory as a result of the evolution =>visual attention hierarchical PQSM (full to rough) PQSM generation integration of multiple stimuli:

16 Recent research attempts Applications of Perceptual Significance Map JND models quality metrics ROI-based compression scalable coding other visual processing, for resource savings/allocation bandwidth, computing power, memory space, display/printing resolution and/or performance enhancement picture quality JND Modulated JND in line with eye tracking results Y C b C r

17 Concluding Remarks interesting areas for further work modeling more temporal effects motion, jerkiness, mean time between errors, etc. significant progress perceptual quality gauges various types of metrics perceptual image/video processing compression other related areas more effective accounting for chrominance effects esp. for non-coding distortion joint modeling with other media audio, text, and so on no-reference situations PSNR not applicable; wider scope of application mobile comm applications PSNR largely irrelevant codec dependent metrics e.g. targeting H.264 artifacts ROI-based scalable coding ROI coding scalability SVC standardization adaptive watermarking authentication error resilience

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