Full-reference quality assessment of stereoscopic images by learning binocular visual properties

Blind light field image quality assessment based on deep meta-learning

In recent years, the use of deep convolutional neural networks (DCNNs) for light field image quality assessment (LFIQA) has gained significant attention. Despite their notable successes, it is widely accepted that training DCNNs heavily depends on a large amount of annotated data. Additionally, convolutional network-based LFIQA methods show a limitation in capturing long-range dependencies. Unfortunately, LFIQA is essentially a typical small-sample problem, leading to existing DCNN-based LFIQA metrics requiring data augmentation but with unsatisfactory performance. To address these issues, this study proposes utilizing the self-attention capability of the Swin Transformer to efficiently capture spatial-angular information while employing meta-learning for small-sample learning in the LFIQA task. Specifically, a collection of LFIQA tasks is gathered, representing different distortions. Then, meta-learning is employed to acquire shared prior knowledge across diverse distortions. Finally, the quality prior model is fine-tuned on a target LFIQA task to obtain the final LFIQA model quickly. Experimental results show that the proposed LFIQA metric achieves high consistency with subjective scores, and outperforms several state-of-the-art LFIQA approaches.

Blind quality assessment of authentically distorted images

Blind image quality assessment (BIQA) of authentically distorted images is a challenging problem due to the lack of a reference image and the coexistence of blends of distortions with unknown characteristics. In this article, we present a convolutional neural network based BIQA model. It encodes the input image into multi-level features to estimate the perceptual quality score. The proposed model is designed to predict the image quality score but is trained for jointly treating the image quality assessment as a classification, regression, and pairwise ranking problem. Experimental results on three different datasets of authentically distorted images show that the proposed method achieves comparable results with state-of-the-art methods in intra-dataset experiments and is more effective in cross-dataset experiments.

Rich Structural Index for Stereoscopic Image Quality Assessment

The human visual system (HVS), affected by viewing distance when perceiving the stereo image information, is of great significance to study of stereoscopic image quality assessment. Many methods of stereoscopic image quality assessment do not have comprehensive consideration for human visual perception characteristics. In accordance with this, we propose a Rich Structural Index (RSI) for Stereoscopic Image objective Quality Assessment (SIQA) method based on multi-scale perception characteristics. To begin with, we put the stereo pair into the image pyramid based on Contrast Sensitivity Function (CSF) to obtain sensitive images of different resolution. Then, we obtain local Luminance and Structural Index (LSI) in a locally adaptive manner on gradient maps which consider the luminance masking and contrast masking. At the same time we use Singular Value Decomposition (SVD) to obtain the Sharpness and Intrinsic Structural Index (SISI) to effectively capture the changes introduced in the image (due to distortion). Meanwhile, considering the disparity edge structures, we use gradient cross-mapping algorithm to obtain Depth Texture Structural Index (DTSI). After that, we apply the standard deviation method for the above results to obtain contrast index of reference and distortion components. Finally, for the loss caused by the randomness of the parameters, we use Support Vector Machine Regression based on Genetic Algorithm (GA-SVR) training to obtain the final quality score. We conducted a comprehensive evaluation with state-of-the-art methods on four open databases. The experimental results show that the proposed method has stable performance and strong competitive advantage.