KEYWORDS: 3D image processing, 3D image reconstruction, Distortion, 3D modeling, Mobile devices, Visualization, Cameras, 3D displays, Optical engineering, 3D acquisition
We propose a novel content-aware three dimensional (3-D) image (two dimensional (2-D)-plus-depth) retargeting method using a 3-D saliency-based energy function that effectively describes perceptual importance of objects in 3-D space. The 2-D and depth images are retargeted together by carving out collinear seam paths corresponding to the one having the least 3-D saliency energy. However, resizing individual objects in different ratios without depth modification can cause surface shape distortion of the 3-D objects. To minimize such distortion, depth values for the objects are adaptively remapped examining depth histogram change. Experimental results indicate that the proposed method can preserve structures of important objects in 3-D space much more effectively than conventional methods.
The conventional hybrid method of block truncation coding (BTC) and differential pulse code modulation (DPCM), namely the DPCM-BTC method, offers better rate-distortion performance than the standard BTC. However, the quantization error in the hybrid method is easily increased for large block sizes due to the use of two representative levels in BTC. In this paper, we first derive a bivariate quadratic function representing the mean squared error (MSE) between the original block and the block reconstructed in the DPCM framework. The near-optimal representatives obtained by quantizing the minimum of the derived function can prevent the rapid increase of the quantization error. Experimental results show that the proposed method improves peak signal-to-noise ratio performance by up to 2dB at 1.5 bit/pixel (bpp) and by 1.2dB even at a low bit rate of 1.1 bpp as compared with the DPCM-BTC method without optimization. Even with the additional computation for the quantizer optimization, the computational complexity of the proposed method is still much lower than those of transform-based compression techniques.
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