SC-IQA: Shift compensation based image quality assessment for DIBR-synthesized views

Abstract : Depth-image-based-rendering (DIBR) has been used to generate the virtual views for Multi-view videos and Free-viewpoint videos. However, the quality assessment of DIBR-synthesized views is very challenging owing to the new types of distortions induced by inaccurate depth maps, dis-occlusions and image inpainting methods. There exist a large number of object shifts and geometric distortions in the synthesized view which the traditional 2D quality metrics may fail to assess. In this paper, we propose a shift compensation based image quality assessment metric (SC-IQA) for DIBR-synthesized views. Firstly, the global geometric shift is compensated roughly by an SURF + RANSAC homography approach. Then, a multi-resolution block matching method, which performs a more accurate matching, is used to precisely compensate the shift and penalize the local geometric distortion as well. In addition, a visual saliency map is also used as a weighting function. To calculate the final overall quality scores, only the worst blocks are utilized since the biggest distortions have the most effects on the overall perceptual quality. The results show that the proposed metric significantly outperforms the state-of-the-art synthesized view dedicated metrics and the conventional 2D IQA metrics.
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Contributor : Shishun Tian <>
Submitted on : Friday, October 19, 2018 - 4:42:42 PM
Last modification on : Friday, November 16, 2018 - 1:29:44 AM
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  • HAL Id : hal-01899637, version 1


Shishun Tian, Lu Zhang, Luce Morin, Olivier Déforges. SC-IQA: Shift compensation based image quality assessment for DIBR-synthesized views. IEEE International Conference on Visual Communications and Image Processing, Dec 2018, Taichung, Taiwan. 〈hal-01899637〉



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