Hierarchical lossless coding of light fields with improved random access

The adoption of light field (LF) technology is often hindered by the large amount of data that is necessary to represent such information. To alleviate such burden, specific coding techniques have been under research in recent years. However, most of them require decoding the whole LF data, even in applications where only a region of the LF visual content needs to be accessed. Therefore, encoding schemes that incorporate scalability and random access capabilities would cater to a wider range of LF use cases, particularly those requiring lossless compression. To enable such type of functionalities, an encoder must be designed so that the decoding process can extract only specific views/regions of the complete LF, according to user options or application requirements. This paper proposes hierarchical Minimum Rate Predictors (H-MRP), a lossless light field codec that provides the aforementioned functionalities. The main contributions herein proposed are: (i) the lossless encoding the LF in multiple hierarchical layers; (ii) the flexible selection of reference images for prediction; and (iii) the use disparity compensation between the reference images. The experimental results show that the proposed encoder provides a good compromise in terms of compression efficiency versus random access penalty, surpassing its best competitor by 25% in terms of random access and by 1 bit-per-pixel in terms of compression efficiency. When optimised to provide the best compression efficiency, the method surpasses current state-of-the-art lossless encoding by up to 3 bits-per-pixel.

Automated summary

This paper proposes a lossless light field codec called hierarchical Minimum Rate Predictors (H-MRP), which provides scalability and random access capabilities for lossless compression of light field data.