[Paper] DeepCNN: Deep CNN for IQA (Image Quality Assessment)
Outperforms SOTA Approaches Such As IQA-CNN
In this story, No-reference Image Quality Assessment with Deep Convolutional Neural Networks (DeepCNN), by City University of Hong Kong, is briefly presented.
In IQA-CNN (2014 CVPR):
- The CNN used only contains one convolution layer which is too shallow.
- 32×32 patch is too small for training as image quality is not homogenous within the image.
In this paper:
- A deeper CNN is proposed to predict the image quality score which outperforms SOTA approaches.
This is a paper in 2016 DSP with about 40 citations. (Sik-Ho Tsang @ Medium)
- DeepCNN: Network Architecture
- Experimental Results
1. DeepCNN: Network Architecture
- The proposed network consists of 31 layers.
- Given a color image, we first sample 224×224 image patches from the original image, and then perform a global contract normalization in each channel by subtracting the mean image of ImageNet.
1.2. Pretrained NIN from 1st to 26th layers
- The pre-trained NIN from the 1st layer to the 26th layer are used.
- MLP convolution layers are used as in red boxes which consists of one traditional convolution layer followed by several convolution layers with 1×1 convolution kernel and ReLU activation function.
- (If interested, please feel free to read NIN.)
1.3. New layers from 27th to 31th layers
- Five new layers are concatenated following the 26th layer which is shown in the blue box.
- Only layer 27 and layer 29 are randomly initialized and their parameters can be easily tuned by fine-tuning process.
- Global average pooling (GAP) is used and then Sigmoid is used.
1.4. Larger Patch Size
- The original color image is resized into 448×448 and fine-tuning the network on 224×224 patches with stride of 112.
- Thus, there are about 5400 image patches for each training process from 600 training images.
2. Experimental Results
- DeepCNN has a competitive results as IQA-CNN and outperforms numerous hand-crafted feature based IQA approaches.