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- W2601548332 abstract "Binocular combination under different distortion types poses a great challenge to three-dimensional image quality assessment (3D-IQA). However, the research works on 3D-IQA with multiple distortion types are very limited. In this paper, we first construct a new multiply distorted stereoscopic image database (NBU-MDSID), which is composed of 270 multiply distorted stereoscopic images and 90 singly distorted stereoscopic images that are corrupted simultaneously and independently by blurring, JPEG compression, and noise injection. We then propose a new multimodal blind metric for quality assessment of multiply distorted stereoscopic images. Inspired by multimodal sparse representation framework, modality-specific dictionaries and the corresponding projection matrices are learned from the singly distorted training database at the training stage, and the testing stage only needs to estimate the quality score based on the reconstruction errors. Experimental results demonstrate the effectiveness of our blind metric." @default.
- W2601548332 created "2017-04-07" @default.
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- W2601548332 date "2017-08-01" @default.
- W2601548332 modified "2023-10-16" @default.
- W2601548332 title "Learning Sparse Representation for No-Reference Quality Assessment of Multiply Distorted Stereoscopic Images" @default.
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- W2601548332 doi "https://doi.org/10.1109/tmm.2017.2685240" @default.
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