Distortion Based Image Quality Index · Distortion Based Image Quality Index Jingwei Guan and...
Transcript of Distortion Based Image Quality Index · Distortion Based Image Quality Index Jingwei Guan and...
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Distortion Based Image Quality Index
Jingwei Guan and Wai-kuen ChamE-mail: {jwguan,wkcham}@ee.cuhk.edu.hk
The Chinese University of Hong Kong
DL-BIQI
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SECTION 1: TASK INTRODUCTION
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Sub: DMOS = 0 DMOS = 47.2859 DMOS = 83.5533
Image Quality Assessment
Notes:DMOS represents the Difference of Mean Opinion Scores(DMOS) of the images. Smaller value better quality.
Different images have different visual quality scores.
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Sub: DMOS = 0 DMOS = 47.2859 DMOS = 83.5533
Obj: Q = 3.57 Q = 45.6398 Q = 86.3695
Image Quality Assessment
Difference = |DMOS – Q|
Smaller difference more accurate IQA metric
Different images have different visual quality scores.
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Appearance of these images are different.
1. Gaussian Blur 2. JPEG 3. JPEG2000 4. White Noise 5. Fast Fading
Images can be distorted by different type.
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Images can be distorted by different type.
Appearance of these images are different.
1. Gaussian Blur 2. JPEG 3. JPEG2000 4. White Noise 5. Fast Fading
DMOS = 0 DMOS = 55.7986 DMOS = 83.2695
Notes:DMOS represents the Difference of Mean Opinion Scores(DMOS) of the images. Smaller value better quality.
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DMOS = 0 DMOS = 47.2859 DMOS = 83.5533
Images can be distorted by different type.
Appearance of these images are different.
1. Gaussian Blur 2. JPEG 3. JPEG2000 4. White Noise 5. Fast Fading
Notes:DMOS represents the Difference of Mean Opinion Scores(DMOS) of the images. Smaller value better quality.
![Page 8: Distortion Based Image Quality Index · Distortion Based Image Quality Index Jingwei Guan and Wai-kuen Cham ... Gaussian Blur 2. ... N Distortion-specific quality estimation models](https://reader031.fdocuments.us/reader031/viewer/2022022523/5b3819807f8b9a4a728cb9e5/html5/thumbnails/8.jpg)
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DMOS = 0 DMOS = 41.2840 DMOS = 58.7538
Images can be distorted by different type.
Appearance of these images are different.
1. Gaussian Blur 2. JPEG 3. JPEG2000 4. White Noise 5. Fast Fading
Notes:DMOS represents the Difference of Mean Opinion Scores(DMOS) of the images. Smaller value better quality.
![Page 9: Distortion Based Image Quality Index · Distortion Based Image Quality Index Jingwei Guan and Wai-kuen Cham ... Gaussian Blur 2. ... N Distortion-specific quality estimation models](https://reader031.fdocuments.us/reader031/viewer/2022022523/5b3819807f8b9a4a728cb9e5/html5/thumbnails/9.jpg)
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DMOS = 0 DMOS = 51.4067 DMOS = 94.4101
Notes:DMOS represents the Difference of Mean Opinion Scores(DMOS) of reference image and distorted images. Smaller value better quality.
Images can be distorted by different type.
Appearance of these images are different.
1. Gaussian Blur 2. JPEG 3. JPEG2000 4. White Noise 5. Fast Fading
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DMOS = 0 DMOS = 48.7926 DMOS = 65.4952
Notes:DMOS represents the Difference of Mean Opinion Scores(DMOS) of reference image and distorted images. Smaller value better quality.
Images can be distorted by different type.
Appearance of these images are different.
1. Gaussian Blur 2. JPEG 3. JPEG2000 4. White Noise 5. Fast Fading
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SECTION 2: RELATED WORK & MOTIVATION
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History of IQA Development
Full-reference (FR) Reduced-reference (RR) No-reference (NR)
Distortion Specific Non-distortion Specific
OUR TASK: No-reference non-distortion specific models
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No-reference non-distortion specific models
Blind image quality index (BIQI)Probability summation model to combine the visual quality estimated for several distortion types together.
The probabilities:Hand-crafted features + support vector machine (SVM)
Distortion-specific visual quality scoresHand-crafted features +support vector regression (SVR)
[1] A.K. Moorthy and A. C. Bovik,“A two-step framework for constructing blind image quality indices,” Signal Processing Letters, IEEE, vol. 17, no. 5, pp. 513–516, 2010.
[1]
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SECTION 3: PROPOSED DL-BIQI METHOD
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Deep Learning based Blind Image Quality Index
Follow the probability summation model
The probabilities:Distortion type classification model ( ) Distortion-specific visual quality scoresN Distortion-specific quality estimation models ( )
DL-BIQI
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DL-BIQI
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SECTION 4: EXPERIMENTS
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Distortion type classification
(a)
(b)
(c)
(d)
Distortion Type classification accuracy:
93.7%
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Visual quality estimation
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Visual quality estimation
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Visual quality estimation
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Thank you