Abstract
In this paper, a new quality metric for depth images is proposed. Unlike the conventional depth metrics which require the additional information such as the ground truth depth image or a stereo image pair, the proposed quality metric demands only a single camera image and its corresponding depth image. In this work, we first empirically observe that the depth distortion is closely related to the local image characteristics. Based on the observation, we introduce a method to assess the local depth distortion for the edge and non-edge regions. Then, the local distortion is adaptively weighted by the Gabor filter and added up to the quality metric for the depth image.
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References
Smolic, A., Mueller, K., Merkle, P., Kauff, P., Wiegand, T.: An overview of available and emerging 3D video formats and depth enhanced stereo as efficient generic solution. In: Proc. 27th Conference on Picture Coding Symposium, pp. 389–392 (2009)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47(1-3), 7–42 (2002)
Klaus, A., Sormann, M., Karner, K.: Segment-based stereo matching using brief propagation and a self-adapting dissimilarity measure. In: Proc. IEEE Conference on Pattern Recongnition, pp. 15–18 (2006)
Wang, Z.-F., Zheng, Z.-G.: A region based stereo matching algorithm using cooperative optimization. In: Proc. IEEE Conference on Computer Vision and Pattern Recongnition, pp. 1–8 (2008)
Fehn, C.: Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV. In: Proc. SPIE, vol. 5291, pp. 93–104 (2004)
D’Angelo, A., Zhaoping, L., Barni, M.: A full-reference quality metric for geometrically distorted images. IEEE Trans. Image Process. 19(4), 867–881 (2010)
Smith, S.M., Brady, J.M.: SUSAN-a new approach to low level image processing. Int. J. Comput. Vision 23(1), 45–78 (1997)
Scharstein, D., Szeliski, R., http://vision.middlebury.edu/stereo
Yang, Q., Yang, R., Davis, J., Nister, D.: Spatial-depth super resolution for range images. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
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Le, TH., Lee, S., Jung, SW., Won, C.S. (2015). Reduced Reference Quality Metric for Depth Images. In: Park, J., Chao, HC., Arabnia, H., Yen, N. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47487-7_18
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DOI: https://doi.org/10.1007/978-3-662-47487-7_18
Publisher Name: Springer, Berlin, Heidelberg
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