View Sub-sampling and Reconstruction for Efficient Light Field Compression
This work addresses compression for practical light field applications like streaming and storage, but it is incremental as it builds on existing methods by exploring view selection.
The paper tackled the problem of light field compression by studying how view sub-sampling and reconstruction strategies affect efficiency, finding optimal methods through evaluation on real-world and synthetic datasets.
Compression is an important task for many practical applications of light fields. Although previous work has proposed numerous methods for efficient light field compression, the effect of view selection on this task is not well exploited. In this work, we study different sub-sampling and reconstruction strategies for light field compression. We apply various sub-sampling and corresponding reconstruction strategies before and after light field compression. Then, fully reconstructed light fields are assessed to evaluate the performance of different methods. Our evaluation is performed on both real-world and synthetic datasets, and optimal strategies are devised from our experimental results. We hope this study would be beneficial for future research such as light field streaming, storage, and transmission.