Rate-distortion analysis of random access for compressed light fields

Abstract

Image-based rendering data sets, such as light fields, require efficient compression due to their large data size, but also easy random access when rendering from the data set. Efficient compression usually depends upon prediction between images, which creates dependencies between them, conflicting with the requirement of having easy random access. Existing light field coders concentrate either on compression efficiency, or use ad hoc methods to design prediction that balances random access and compression efficiency requirements. In this paper, we study this joint problem of compression efficiency and random access. We propose a model for light field image generation, light field image coding and rendering novel views from these light field images. We present a view-dependent rate-distortion measure that allows us to consider random access and compression efficiency simultaneously. We compare the theoretical results from the model with the experimental results from our DCT-based coder and show that they qualitatively give similar results. Finally, we suggest how. with this model, we can better optimize the prediction dependency structure in our coder for random access and compression efficiency performance.