DB-CNN: Deep Bilinear Convolutional Neural Network (Image Quality Assessment)

mediumThis post was originally published by Sik-Ho Tsang at Medium [AI]

1 CNN for Synthetic Distortions, 1 CNN for Authentic Distortions, Outperforms DeepIQA (DIQaM & WaDIQaM)

Image for post
Waterloo Exploration Database (From https://ece.uwaterloo.ca/~k29ma/exploration/)

In this story, Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network (DB-CNN), by Wuhan University, New York University, and University of Waterloo, is presented. I read this because I recently study IQA/VQA. In this paper:

  • For synthetic distortions, a CNN is pre-trained to classify image distortion type and level.
  • For authentic distortions, a pretrained CNN for image classification is adopted.
  • The features from the two CNNs are pooled bilinearly into a unified representation for final quality prediction.
  • The entire model is fine-tuned on target subject-rated databases.

This is a paper in 2020 TCVST where TCSVT has a high impact factor of 4.133. (Sik-Ho Tsang @ Medium)

Outline

  1. CNN for Synthetic Distortions
  2. CNN for Authentic Distortions
  3. DB-CNN by Bilinear Pooling
  4. Ablation Study
  5. Experimental Results

1. CNN for Synthetic Distortions

Image for post
Examples of synthetic distortions: (a) Gaussian blur. (b) White Gaussian noise. (c) JPEG compression. (d) JPEG2000 compression. (e) Contrast stretching. (f) Pink noise. (g) Image color quantization with dithering. (h) Over-exposure. (i) Under-exposure.
  • A CNN is pre-trained to classify the distortion type and the degradation level. This pre-training strategy is to offer perceptually more meaningful initializations.
  • M-class one-hot vector is used as ground-truth. In this case, M = 39, which corresponds to seven distortion types with five levels and two distortion types with two levels.
Image for post
S-CNN Inspired by VGG-16
  • Inspired by the VGG-16 network architecture [21], a CNN for synthetic distortions (S-CNN) is designed with a similar structure subject to some modifications.
  • All convolutions have a kernel size of 3×3.
  • Cross-entropy loss is used.

2. CNN for Authentic Distortions

  • VGG-16 that has been pre-trained for the image classification task on ImageNet, is used to extract relevant features for authentically distorted images.

3. DB-CNN by Bilinear Pooling

Image for post
DB-CNN: Network Architecture
  • Bilinear pooling to combine S-CNN for synthetic distortions and VGG-16 for authentic distortions into one unified model.
  • Denote the representations from S-CNN and VGG-16 by Y1 and Y2, the bilinear pooling of Y1 and Y2 is formulated as:
Image for post
  • Then B is fed to a fully connected layer with one output value to predict the image quality score.

4. Ablation Study

Image for post
Average SRCC
  • S-CNN or VGG-16 alone is act as baseline. S-CNN and VGG-16 can only deliver promising performance on synthetic and authentic databases, respectively.
  • DB-CNN with concatenation used instead of bilinear pooling.
  • Two DB-CNN models are trained, one from scratch and the other using the distortion type information only during pre-training S-CNN.
  • It can be seen that, with perceptually more meaningful initializations, DB-CNN achieves better performance. DB-CNN is capable of handling both synthetic and authentic distortions.

5. Experimental Results

5.1. SOTA Comparison

Image for post
Average SRCC and PLCC Across Ten Sessions on LIVE Challenge Database
  • LIVE contains 779 distorted images synthesized from 29 reference images with five distortion types.
  • CSIQ is composed of 866 distorted images generated from 30 reference images, including six distortion types.
  • TID2013 consists of 3; 000 distorted images from 25 reference images with 24 distortion types at five degradation levels.
  • LIVE MD contains 450 images generated from 15 source images under two multiple distortion scenarios.
  • LIVE CL is an authentic IQA database, which contains 1,162 images captured from diverse real-world scenes by numerous photographers.
  • Two splits are used. 80% is used for fine-tuning DB-CNN. 20% is used for testing.
  • While all competing models, such as DeepIQA (DIQaM & WaDIQaM) achieve comparable performance on LIVE, their results on CSIQ and TID2013 are rather diverse.
  • DB-CNN performs favorably although it does not include multiply distorted images for pre-training, indicating that DB-CNN generalizes well to slightly different distortion scenarios.
  • The success of DB-CNN on LIVE Challenge verifies the relevance between the high-level features from VGG-16 and the authentic distortions.

In summary, DB-CNN achieves superior performance on both synthetic and authentic IQA databases.

5.2. Individual Distortion Types

Image for post
Average SRCC and PLCC of Individual Distortion Types Across Ten Sessions on LIVE
Image for post
Average SRCC and PLCC of Individual Distortion Types Across Ten Sessions on CSIQ
Image for post
Average SRCC of Individual Distortion Types Across Ten Sessions on TID2013
  • On CSIQ, DB-CNN outperforms other counterparts by a large margin, especially for pink noise and contrast change, validating the effectiveness of pre-training in DB-CNN.
  • Although many distortion types are not synthesized as in TID2013, it can be found that DB-CNN performs well on unseen distortion types that exhibit similar artifacts in our pre-training set.
  • DB-CNN generalizes well to unseen distortions with similar perceived artifacts. In addition, all other non-reference (NR) models fail in three distortion types on TID2013.

5.3. Across Different Databases

Image for post
  • It can be seen that models trained on LIVE are much easier to generalize to CSIQ and vice versa than other cross-database pairs.
  • When trained on TID2013 and tested on the other two synthetic databases, DB-CNN significantly outperforms the rest models.
  • Models trained on synthetic databases do not generalize to the authentic LIVE Challenge Database. Despite this, DB-CNN still achieves higher prediction accuracies under such a challenging experimental setup.

5.4. Waterloo Exploration Database

Image for post
  • Authors also propose a database, Waterloo Exploration Database.
  • Waterloo Exploration Database and the PASCAL VOC Database, are used, where the images are synthesized with nine distortion types and two to five distortion levels.
  • The pristine/distorted image discriminability test (D-Test), the list-wise ranking consistency test (L-Test), and the pairwise preference consistency test (P-Test), are performed.
  • To ensure the independence of image content during training and testing, the S-CNN stream is re-trained in DB-CNN using the distorted images generated from the PASCAL VOC Database only.
  • It is observed that DB-CNN is competitive in all the three tests.
Spread the word

This post was originally published by Sik-Ho Tsang at Medium [AI]

Related posts