Lenslet image compression using adaptive macropixel prediction
Haixu Han, Xin Jin, Qionghai Dai · 2017
In this paper, an efficient compression method is proposed for lenslet images captured by plenoptic cameras for recording the spatial and angular light information at a super-high-resolution. After applying a reversible image reshaping method to the lenslet image, a reshaped and regularized image will be generated and compressed by the video codec comprising the proposed adaptive macropixel prediction mode. Based on the analysis of spatial correlations among adjacent macropixels, two spatial prediction modes are proposed as: multi-block weighted prediction mode and co-located single-block prediction mode, to predict the coding unit by minimizing the coding cost. The multi-block weighted prediction is formulated by minimizing the Euclidean distance between the coding unit and co-located blocks in the macropixel structure. Performance evaluations have shown that the proposed method achieves 50.9% of bit-savings on average compared to HEVC. It also outperforms state-of-the-art coding methods drastically.