Adaptive two-layer light field compression scheme based on sparse reconstruction
Xinjue Hu, Jingming Shan, Yu Liu, Lin Zhang · 2019
As a new form of volumetric media, the technology of light field and its compression has gradually become the research hotspots in academia. The scheme of compressing using the sparsity of the light field is a very promising idea, which has the characteristics of high compression rate and is not affected by the occlusion of scene objects. However, the instability of the reconstruction algorithm's performance on different datasets limits the further application of this solution. Since the quality of the decompression outputs will be limited below the reconstruction result, the poor performance of the reconstruction algorithm on some light field images will result in a very low PSNR upper limit for the compression scheme. This paper finds that the main reason for this performance problem is the poor ability of the algorithm to process the high-frequency components of the light field. And in order to solve it, an adaptive two-layer light field compression scheme is presented. The proposed scheme separates the high-frequency components and the low-frequency components of the light field so that they can be independently compressed. Through the adaptive adjustment, the data of different frequency component can adopt different compression strategies, so that the performance of the proposed scheme can be optimal. Experiments with multiple datasets1 show that the proposed scheme can break the upper limit of PSNR caused by sparse reconstruction and is capable to provide decompression results above 40 dB. It also achieves significant improvement in compression efficiency under diverse requirements.