Efficient Light Field Transmission via Enhanced Resampling Reconstruction and Angular Attention Estimation

Light field (LF) imaging technology has significantly enhanced immersive virtual reality experiences and advanced computer vision tasks like depth estimation and 3D reconstruction. However, the massive data volume of light field images (LFIs) presents a transmission challenge. To address this issue, we propose a novel intelligent LF transmission approach integrating enhanced resampling reconstruction and angular attention estimation. The proposed enhanced resampling reconstruction method reduces spatial redundancy by downsampling before transmission and upsampling afterward, aided by a pre-calculated residual map to minimizing quality loss. To reduce angular redundancy while preserving perceptual quality, we designed a LF angular attention estimation network employing specifically designed angular attention kernels to guide differential transmission within the angular domain. To train this network, we construct the first LF eye-tracking dataset, featuring numerous of samples, diverse realistic scenes, and a wealth of data elements, particularly real user angular attention data, establishing it a key benchmark in LF transmission. Extensive experiments demonstrate that our transmission strategy decreases transmission time by 97.3% with only a 3.1% perceptual quality loss. Subjective experiments further confirm the superior performance of our method. An implementation of this paper and the constructed dataset are available at https://github.com/VincentQQu/LF-Transmission.