Light Field Image Compression via CNN-Based EPI Super-Resolution and Decoder-Side Quality Enhancement

Because of the capacity of capturing both the spatial and angular information of the light rays simultaneously, light field images (LFIs) contain richer scene information compared with conventional images, but at the cost of huge volume. This paper proposes a novel LFI sparse compression framework driven by convolutional neural network (CNN). The epipolar plane image (EPI) super-resolution is for compensating the information loss caused by sparse sampling and the decoder-side sub-aperture images (SAIs) quality enhancement is for compensating the information loss caused by lossy compression. Specifically, we choose those SAIs both in odd rows and odd columns as our key SAIs and compress them using standard video encoder. For those non-key SAIs, we predict them using decompressed key SAIs by taking advantage of the special structure of EPI. The low-resolution EPIs generated from the sparse SAIs are super-resolved by a CNN and the outputs, high-resolution EPIs, are used to rebuild the dense SAIs. Moreover, in order to improve the quality of the predicted SAI, we add decoder-side quality enhancement before prediction. We propose a multi-scale dense residual network (MSDRN) to implement both EPI super-resolution and quality enhancement. Transfer learning strategies are used to improve the training performance of quality enhancement. The experimental results show the superior performance of the proposed framework over existing methods in terms of rate-distortion performance.

Abstract licence: CC BY-NC-ND

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