Scalable Coding of Plenoptic Images by Using a Sparse Set and Disparities PROJECT TITLE :Scalable Coding of Plenoptic Images by Using a Sparse Set and DisparitiesABSTRACT: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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Microbloggers as Sensors for Public Transport Breakdowns Robust stabilisation of power systems with random abrupt changes