Abstract:
Light field image (LFI) quality assessment is becoming more important to guide immersive media acquisition, processing, and use. Due to LFI’s high dimensionality, quality assessment must consider quality degradation in both spatial and angular dimensions.
Thus, we propose a tensor-based No-reference Light Field image Quality evaluator, Tensor-NLFQ. Since the LFI is a low-rank 4D tensor, Tucker decomposition yields the principal components of four oriented sub-aperture view stacks.
Then, the Principal Component Spatial Characteristic (PCSC) measures LFI’s spatial-dimensional quality based on global naturalness and local frequency.
Finally, the Tensor Angular Variation Index (TAVI) analyzes structural similarity between the first principal component and each view in the view stack to measure angular consistency quality.
The proposed Tensor-NLFQ model outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms in extensive experiments on four publicly available LFI quality databases.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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