PROJECT TITLE :

Scalable Coding of Plenoptic Images by Using a Sparse Set and Disparities

ABSTRACT:

One among the sunshine field capturing techniques is that the focused plenoptic capturing. By putting a microlens array in front of the photosensor, the centered plenoptic cameras capture each spatial and angular information of a scene in each microlens image and across microlens images. The capturing ends up in a important amount of redundant information, and therefore the captured image is usually of a large resolution. A coding theme that removes the redundancy before coding will be of advantage for efficient compression, transmission, and rendering. During this paper, we propose a lossy coding scheme to efficiently represent plenoptic pictures. The format contains a sparse image set and its associated disparities. The reconstruction is performed by disparity-based interpolation and inpainting, and therefore the reconstructed image is later employed as a prediction reference for the coding of the total plenoptic image. As an outcome of the illustration, the proposed scheme inherits a scalable structure with three layers. The results show that plenoptic images are compressed efficiently with over 60 % bit rate reduction compared with High Potency Video Coding intra coding, and with over 20 percent compared with an High Potency Video Coding block copying mode.