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The quality assessment of light field images presents new challenges to conventional compression methods, as the spatial quality is affected by the optical distortion of capturing devices, and the angular consistency affects the performance of dynamic rendering applications. In this paper, we propose a two-pass encoding system for pseudo-temporal sequence based light field image compression with a novel frame level bit allocation framework that optimizes spatial quality and angular consistency simultaneously. Frame level rate-distortion models are estimated during the first pass, and the second pass performs the actual encoding with optimized bit allocations given by a two-step convex programming. The proposed framework supports various encoder configurations. Experimental results show that comparing to the anchor HM 16.16 (HEVC reference software), the proposed two-pass encoding system on average achieves 11.2% to 11.9% BD-rate reductions for the all-intra configuration, 15.8% to 32.7% BD-rate reductions for the random-access configuration, and 12.1% to 15.7% BD-rate reductions for the low-delay configuration. The resulting bit errors are limited, and the total time cost is less than twice of the one-pass anchor. Comparing with our earlier low-delay configuration based method, the proposed system improves BD-rate reduction by 3.1% to 8.3%, reduces the bit errors by more than 60%, and achieves more than 12x speed up.
Compared with conventional image and video, light field images introduce the weight channel, as well as the visual consistency of rendered view, information that has to be taken into account when compressing the pseudo-temporal-sequence (PTS) created
Light field (LF) representations aim to provide photo-realistic, free-viewpoint viewing experiences. However, the most popular LF representations are images from multiple views. Multi-view image-based representations generally need to restrict the ra
Learning-based light field reconstruction methods demand in constructing a large receptive field by deepening the network to capture correspondences between input views. In this paper, we propose a spatial-angular attention network to perceive corres
A good distortion representation is crucial for the success of deep blind image quality assessment (BIQA). However, most previous methods do not effectively model the relationship between distortions or the distribution of samples with the same disto
Existing blind image quality assessment (BIQA) methods are mostly designed in a disposable way and cannot evolve with unseen distortions adaptively, which greatly limits the deployment and application of BIQA models in real-world scenarios. To addres