Lightweight macro-pixel quality enhancement network for light field images compressed by versatile video coding

Previous research demonstrated that filtering Macro-Pixels (MPs) in a decoded Light Field Image (LFI) sequence can effectively enhances the quality of the corresponding Sub-Aperture Images (SAIs). In this paper, we propose a deep-learning-based quality enhancement model following the MP-wise processing approach tailored to LFIs encoded by the Versatile Video Coding (VVC) standard. The proposed novel Res2Net Quality Enhancement Convolutional Neural Network (R2NQE-CNN) architecture is both lightweight and powerful, in which the Res2Net modules are employed to perform LFI filtering for the first time, and are implemented with a novel improved 3D-feature-processing structure. The proposed method incorporates only 205K model parameters and achieves significant Y-BD-rate reductions over VVC of up to 32%, representing a relative improvement of up to 33% compared to the state-of-the-art method, which has more than three times the number of parameters of our proposed model. • Filtering macro-pixels in light field images compressed by versatile video coding. • Novel improved 3D Res2Net module with introduced spatial attention. • Lightweight quality enhancement network which incorporates only 205K parameters. • Achieving up to 32% BD-rate savings over versatile video coding.