ABSTRACT
Light field imaging has increasingly gained attention in the research community for its ability to provide a richer and more complete representation of visual scenes by capturing both spatial and angular information. However, the massive data volume generated by light field images poses significant challenges in terms of storage and transmission, particularly in bandwidth-limited or real-time environments. This work proposes a novel approach for compressing light field data by integrating Sparse Sampling techniques with the Versatile Video Coding (VVC) standard. The proposed method leverages the Discrete Cosine Transform (DCT) to generate a sparse representation of light field sub-aperture images, followed by random sparse sampling to reduce the dimensionality of the data. A reconstruction process using orthogonal matching pursuit ensures high-fidelity recovery of the light field prior to VVC encoding. The performance of the proposed solution is evaluated using samples of the HCI Dataset, and results demonstrate significant reductions in data size while maintaining visual quality in up to 38db (PSNR) and 0,95 SSIM. The integration of sparse sampling and VVC coding demonstrates promising results for light field compression, enabling its deployment in high-speed communication networks.