Smart devices for image/video sensing are needed to work within the constraints of limited bandwidth and low computing capabilities. In this context, Block based Compressive Sensing (BCS) emerged as the most viable me...
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Smart devices for image/video sensing are needed to work within the constraints of limited bandwidth and low computing capabilities. In this context, Block based Compressive Sensing (BCS) emerged as the most viable method for balancing image/video quality and transmission bandwidth computing overheads. However, in comparison with conventional image and video acquisition systems, BCS cannot reduce the bitrate due to its straightforward nature of system of linear equations, which still incurs high transmission and storage overhead. To address this shortcoming, in this brief we propose a novel Near Lossless predictivecoding (NLPC) approach to compress BCS measurements. The NLPC method encodes the prediction error measurement between the target and current measurement, resulting in lower data size. We designed and implemented a complete BCS integrated with NLPC with scalar quantization (BCS-NLPC-SQ) and studied the image quality at different compression ratios with varying block sizes. The BCS-NLPC-SQ method can improve roughly on an average PSNR of +3.06 dB and the average SSIM gain of +0.11 with respect to the existing works. The synthesis results shows that, BCS-NLPC-SQ requires 83.01%, 69.03%, 53.26%, and 14.45% less area, power, ADP and PDP over JPEG compression and we have achieved an additional compression of up to 56.25% in the best case. Our proposed BCS-NLPC-SQ method outperformed the existing methods in terms of PSNR, SSIM, and bpp.
A complete encoding solution for efficient intra-based depth map compression is proposed in this paper. The algorithm, denominated predictive depth coding (PDC), was specifically developed to efficiently represent the...
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A complete encoding solution for efficient intra-based depth map compression is proposed in this paper. The algorithm, denominated predictive depth coding (PDC), was specifically developed to efficiently represent the characteristics of depth maps, mostly composed by smooth areas delimited by sharp edges. At its core, PDC involves a directional intra prediction framework and a straightforward residue coding method, combined with an optimized flexible block partitioning scheme. In order to improve the algorithm in the presence of depth edges that cannot be efficiently predicted by the directional modes, a constrained depth modeling mode, based on explicit edge representation, was developed. For residue coding, a simple and low complexity approach was investigated, using constant and linear residue modeling, depending on the prediction mode. The performance of the proposed intra depth map coding approach was evaluated based on the quality of the synthesized views using the encoded depth maps and original texture views. The experimental tests based on all intra configuration demonstrated the superior rate-distortion performance of PDC, with average bitrate savings of 6%, when compared with the current state-of-the-art intra depth map coding solution present in the 3D extension of a high-efficiency video coding (3D-HEVC) standard. By using view synthesis optimization in both PDC and 3D-HEVC encoders, the average bitrate savings increase to 14.3%. This suggests that the proposed method, without using transform-based residue coding, is an efficient alternative to the current 3D-HEVC algorithm for intra depth map coding.
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