Lossy compression of lenslet images from plenoptic cameras combining sparse predictive coding and JPEG 2000

• Published in: 2017 IEEE International Conference on Image Processing (ICIP) pp. 4567 - 4571
• Main Authors: Tabus, Ioan, Helin, Petri, Astola, Pekka
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2017
• Subjects: Bit rate; depthmap compression; Encoding; Image coding; Image color analysis; Image segmentation; image warping; Indexes; JPEG 2000; JPEG-PLENO; lenslet image compression; sparse prediction; Transform coding
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2017.8297147

This paper proposes a lenslet image compression method scalable from low bitrates to fully lossless. The subaperture images are split into two sets: a set of reference views, encoded by a standard lossy or lossless compressor, and the set of dependent views, which are reconstructed by sparse prediction from the reference set using the geometrical information from the depth map. The set of reference views may contain all views and all views may also be dependent views, in which case the sparse predictive stage does not reconstruct from scratch the views, but it refines in a sequential order all views by combining in an optimal way the information about the same region existing in neighbor views. The encoder transmits to the decoder a segmented version of the scene depthmap, the encoded versions of the reference views, displacements for each region from the central view to each of the dependent views, and finally the sparse predictors for each region and each dependent view. The scheme can be configured to ensure random access to the dependent views, while the reference views are compressed in a backward compatible way, e.g., using JPEG 2000. The experimental results show performance better than that of the baseline standard compressor used, JPEG 2000.