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Open AccessArticle

Depth Image Coding Using Entropy-Based Adaptive Measurement Allocation

Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
College of Information Engineering, North China University of Technology, Beijing 100144, China
Electronic Information and Engineering College, Taiyuan University of Science and Technology, Taiyuan 030024, China
Author to whom correspondence should be addressed.
Entropy 2014, 16(12), 6590-6601;
Received: 20 October 2014 / Revised: 7 December 2014 / Accepted: 12 December 2014 / Published: 17 December 2014
Differently from traditional two-dimensional texture images, the depth images of three-dimensional (3D) video systems have significant sparse characteristics under the certain transform basis, which make it possible for compressive sensing to represent depth information efficiently. Therefore, in this paper, a novel depth image coding scheme is proposed based on a block compressive sensing method. At the encoder, in view of the characteristics of depth images, the entropy of pixels in each block is employed to represent the sparsity of depth signals. Then according to the different sparsity in the pixel domain, the measurements can be adaptively allocated to each block for higher compression efficiency. At the decoder, the sparse transform can be combined to achieve the compressive sensing reconstruction. Experimental results have shown that at the same sampling rate, the proposed scheme can obtain higher PSNR values and better subjective quality of the rendered virtual views, compared with the method using a uniform sampling rate. View Full-Text
Keywords: depth image coding; entropy; 3D video system; compressive sensing depth image coding; entropy; 3D video system; compressive sensing
MDPI and ACS Style

Bai, H.; Zhang, M.; Liu, M.; Wang, A.; Zhao, Y. Depth Image Coding Using Entropy-Based Adaptive Measurement Allocation. Entropy 2014, 16, 6590-6601.

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