Consumer Image Quality Prediction using Recurrent Neural Networks for Spatial Pooling
This work addresses image quality assessment for consumer applications, but it is incremental as it builds on existing CNN-based methods by adding an RNN component.
The paper tackles the challenge of predicting subjective image quality for high-resolution images by proposing a model that uses a recurrent neural network for spatial pooling of CNN-extracted features, mimicking human visual attention. The method achieves competitive accuracy against state-of-the-art benchmarks and performs consistently across different image resolutions.
Promising results for subjective image quality prediction have been achieved during the past few years by using convolutional neural networks (CNN). However, the use of CNNs for high resolution image quality assessment remains a challenge, since typical CNN architectures have been designed for small resolution input images. In this study, we propose an image quality model that attempts to mimic the attention mechanism of human visual system (HVS) by using a recurrent neural network (RNN) for spatial pooling of the features extracted from different spatial areas (patches) by a deep CNN-based feature extractor. The experimental study, conducted by using images with different resolutions from two recently published image quality datasets, indicates that the quality prediction accuracy of the proposed method is competitive against benchmark models representing the state-of-the-art, and the proposed method also performs consistently on different resolution versions of the same dataset.