No abstract

The recently introduced coder based on regionadaptive hierarchical transform (RAHT) for the compression of point clouds attributes [1], was shown to have a performance competitive with the state-of-the-art, while being much less complex. In [1], top performance was achieved using arithmetic coding (AC), while adaptive run-length Golomb-Rice (RLGR) coding was presented as a lower-performance lower-complexity alternative. However, we have found that by reordering the RAHT coefficients we can largely increase the runs of zeros and significantly increase the performance of the RLGR-based RAHT coder. As a result, the new coder, using ordered coefficients, was shown to outperform all other coders, including AC-based RAHT, at an even lower computational cost. We present new results and plots that should enhance those in [1] to include the new results for RLGR-RAHT. We risk to say, based on the results herein, that RLGR-RAHT with sorted coefficients is the new state-of-the-art in point cloud compression.Index Terms-point cloud, compression, 3D immersive video, free-viewpoint video, RAHT.

In this paper we propose a post-processing pipeline to recover accurately the views (light-field) from the raw data of a plenoptic camera such as Lytro and to estimate disparity maps in a novel way from such a light-field. First, the microlens centers are estimated and then the raw image is demultiplexed without demosaicking it beforehand. Then, we present a new block-matching algorithm to estimate disparities for the mosaicked plenoptic views. Our algorithm exploits at best the configuration given by the plenoptic camera: (i) the views are horizontally and vertically rectified and have the same baseline, and therefore (ii) at each point, the vertical and horizontal disparities are the same. Our strategy of demultiplexing without demosaicking avoids image artifacts due to view cross-talk and helps estimating more accurate disparity maps. Finally, we compare our results with state-of-the-art methods.

No abstract

Thick films of humic substances (HS) from peat are evaluated for their electrical and humidity properties for use in humidity sensing. The thickness (10-50 mm) of the films is controlled by the amount of deposited solution on the polyester substrate. The samples exhibit typically two regions in their sensitivity curve when tested in the relative humidity (RH) range of 50-95%. The sensitivity (kW/%RH) ranges from 2.81W/%RH to 19.18 W/%RH for higher to lower thickness.