Minimum description length sparse modeling and region merging for lossless plenoptic image compression
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||IEEE Journal on Selected Topics in Signal Processing|
|Publication status||Published - 2017|
|Publication type||A1 Journal article-refereed|
This paper proposes a complete lossless compression method for exploiting the redundancy of rectified light-field data. The light-field data consists of an array of rectified subaperture images, called for short views, which are segmented into regions according to an optimized partition of the central view. Each region of a view is predictively encoded using a specifically designed sparse predictor, exploiting the smoothness of each color component in the current view, and the cross-similarities with the other color components and already encoded neighbor views. The views are encoded sequentially, using a spiral scanning order, each view being predicted based on several similar neighbor views. The essential challenge for each predictor becomes choosing the most relevant regressors, from a large number of possible regressors belonging to the neighbor views. The proposed solution here is to couple sparse predictor design and minimum description length (MDL) principle, where the data description length is measured by an implementable code length, optimized for a class of probability models. The paper introduces a region merging segmentation under MDL criterion for partitioning the views into regions having their own specific sparse predictors. In experiments, several fast sparse design methods are considered. The proposed scheme is evaluated over a database of plenoptic images, achieving better lossless compression ratios than straightforward usage of standard image and video compression methods for the spiral sequence of views.
- Cameras, Data structures, Encoding, Image coding, Image color analysis, Image segmentation, light-field coding, lossless compression, minimum description length segmentation, plenoptics, sparse prediction, Spirals