Full Reference Quality Assessment for Image Retargeting Based on Natural Scene Statistics Modeling and Bi-Directional Saliency Similarity

Image retargeting quality assessment: A survey

With the increasing variety of display devices, image retargeting has become an indispensable technology for adjusting the aspect ratio of images to adapt to different display terminals. Since the retargeting operation would cause geometric distortion and content loss of the image, the image retargeting quality assessment (IRQA) is necessary to guide the retargeting algorithm’s optimization, selection, and design. Our paper mainly works for systematically reviewing the state-of-the-art technologies in IRQA. And then, this paper further discusses image registration algorithms for matching the original image and the retargeted image. Next, we investigate the feature measurement methods for image retargeting quality evaluation. To facilitate the quantitative assessment of the IRQA methods, this paper gives a list of publicly open datasets and the performance of the mainstream methods. Finally, some promising research directions towards IRQA are pointed out. From this survey, engineers from the industry may find skills to improve their image retargeting systems, and researchers from academia may find ideas to conduct some innovative work.

No-Reference Quality Assessment for View Synthesis Using DoG-based Edge Statistics and Texture Naturalness

View synthesis is a key technique in free-viewpoint video, which renders virtual views based on texture and depth images. The distortions in synthesized views come from two stages, i.e., the stage of the acquisition and processing of texture and depth images, and the rendering stage using depth-image-based-rendering (DIBR) algorithms. The existing view synthesis quality metrics are designed for the distortions caused by a single stage, which cannot accurately evaluate the quality of the entire view synthesis process. With the considerations that the distortions introduced by two stages both cause edge degradation and texture unnaturalness, and the Difference-of-Gaussian (DoG) representation is powerful in capturing image edge and texture characteristics by simulating the center-surrounding receptive fields of retinal ganglion cells of human eyes, this paper presents a no-reference quality index for Synthesized views using DoG-based Edge statistics and Texture naturalness (SET). To mimic the multi-scale property of the Human Visual System (HVS), DoG images are first calculated at multiple scales. Then the orientation selective statistics features and the texture naturalness features are calculated on the DoG images and the coarsest scale image, producing two groups of quality-aware features. Finally, the quality model is learnt from these features using the random forest regression model. Experimental results on two view synthesis image databases demonstrate that the proposed metric is advantageous over the relevant state-of-the-arts in dealing with the distortions in the whole view synthesis process.

Completely blind image quality assessment via image gray-scale fluctuations and fractal dimension analysis

State-of-the-art no-reference image quality assessment methods usually learn to evaluate image quality by regression from the human subjective scores of a training set. Their dependence on the regression algorithm and human subjective scores may limit the practical application of such methods. In this paper, we propose a completely blind image quality assessment method that is highly unsupervised and training free. We first use a specific image primitive to analyze the image gray-scale fluctuation and use this result as one of the image quality assessment features. The box-counting method is then used to evaluate the image fractal dimension, and the result is used as the other feature. Finally, the two features are combined together, and a formula is introduced to calculate a comprehensive image quality feature, which is used to measure the image quality. Experimental results on four open databases show that the newly proposed method correlates well with the human subjective judgments of diversely distorted images.