View Sub-sampling and Reconstruction for Efficient Light Field Compression

TL;DR

This paper investigates view sub-sampling and reconstruction strategies to improve light field compression efficiency, evaluating various methods on real and synthetic datasets to identify optimal approaches for practical applications.

Contribution

It introduces a comprehensive study of sub-sampling and reconstruction strategies for light field compression, highlighting their impact on compression performance and proposing optimal methods based on experimental results.

Findings

Optimal sub-sampling strategies vary between datasets.

Reconstruction quality significantly affects compression efficiency.

Experimental results guide future light field streaming and storage methods.

Abstract

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.