Low Bitrate Light Field Video Compression with Two-step Refinement Reconstruction
Abstract: Light Fields (LFs) are characterized by high dimension, complex structure and large amount of data. Therefore, the efficient compression of LF videos faces challenges. Existing methods use the traditional multi-view video encoder to compress LF videos, but the bitrates are still high. To this end, we propose to only encode sparse key view sequences in LF video for low bitrates. The proposed similarity-based prediction structure fully exploits the spatial-angular-temporal correlations in LF videos. At the decoder side, we propose to reconstruct the uncoded non-key view sequences by a two-step refinement reconstruction network. To avoid error propagation, the proposed reconstruction network firstly refines the coded key view sequences by reducing the compression artifacts. With regard to the distortion caused by warping, the network further refines the texture details of the synthesized non-key view sequences. The experiment results prove that the proposed algorithm performs superior at low bitrates.
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