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A Novel Approach to Depth Distortion Score Computation in 3-D Image Retargeting

Authors: Jagtap, Mahendra T.; Dr Dinesh Kumar Jawalkar;

A Novel Approach to Depth Distortion Score Computation in 3-D Image Retargeting

Abstract

{"references": ["Basha, T. D., Moses, Y., & Avidan, S. (2013). Stereo seam carving a geometrically consistent approach. IEEE transactions on pattern analysis and machine intelligence, 35(10), 2513-2525.", "Lei, J., Wu, M., Zhang, C., Wu, F., Ling, N., & Hou, C. (2017). Depthpreserving stereo image retargeting based on pixel fusion. IEEE transactions on multimedia, 19(7), 1442-1453.", "Liu, Y., Sun, L., & Yang, S. (2015). A retargeting method for stereoscopic 3D video. Computational Visual Media, 1(2), 119-127.", "Chang, C. H., Liang, C. K., & Chuang, Y. Y. (2011). Content-aware display adaptation and interactive editing for stereoscopic images. IEEE Transactions on Multimedia, 13(4), 589-601.", "Yan, B., Li, K., Yang, X., & Hu, T. (2014). Seam searching-based pixel fusion for image retargeting. IEEE Transactions on Circuits and Systems for Video Technology, 25(1), 15-23.", "Fang, Y., Zeng, K., Wang, Z., Lin, W., Fang, Z., & Lin, C. W. (2014). Objective quality assessment for image retargeting based on structural similarity. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 4(1), 95-105.", "Jung, Y. J., Sohn, H., Lee, S. I., & Ro, Y. M. (2014). Visual comfort improvement in stereoscopic 3D displays using perceptually plausible assessment metric of visual comfort. IEEE Transactions on Consumer Electronics, 60(1), 1-9.", "Wang, J., Wang, S., Ma, K., & Wang, Z. (2016). Perceptual depth quality in distorted stereoscopic images. IEEE Transactions on Image Processing, 26(3), 1202-1215.", "Ma, L., Xu, L., Zhang, Y., Yan, Y., & Ngan, K. N. (2016). No-reference retargeted image quality assessment based on pairwise rank learning. IEEE Transactions on Multimedia, 18(11), 2228-2237.", "Ryu, S., & Sohn, K. (2013). Noreference quality assessment for stereoscopic images based on binocular quality perception. IEEE Transactions on Circuits and Systems for Video Technology, 24(4), 591-602.", ". Shao, F., Shen, L., Jiang, Q., Li, F., & Ho, Y. S. (2019). User Controllable Content Retargeting and Depth Adaptation for Stereoscopic Display. IEEE Access, 7, 22541- 22553.", ". Li, D., Cao, J., & Liu, Y. (2018). Stereoscopic 3D Image Retargeting Quality Assessment. Acta Microscopica, 27(4)."]}

3D Image quality degradation can be observed by measuring the depth distortion of an image. The depth distortion is based on the adjustment of image aspect ratio. This aspect ratio can be increased in order to achieve the better depth, which subsequently reduces the degradation in the 3D image. This phenomenon is applied on 3D animated movies by abolishing the blurriness in the pair of images. These pair of images are called as ‘stereo images. The stereo images are found in the pairs such as left stereo image and right stereo image. In 3D animated movies, different viewpoints are designed for left and right eye. So, when anyone observes the movie with naked eyes, the accuracy in visibility gets affected. This problem can be eradicated by wearing the chemically affected 3D sterilize goggles for a short span of time. The proposed method of The Disparity Map Acquisition (DMA) can achieve the task in some significant way, which gives rise to the depth distortion with improved disparity matrix. In this paper, we emphasis on depth score enhancement in 3D stereo images retargeting to accomplish the acceptable 3D images with improved depth distortion score. The experimental results show the stereo seam carving that neglects the unwanted image patches in order to generate an acceptable 3D stereo image with the better visual effects. This may lead to the non-usability of 3D sterilize goggles and eventually helps and reduces the burden on Indian economy.

Keywords

Depth distortion, Stereo image, Disparity matrix, Seam carving.

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