Abstract
Most cell phones today can receive and display video content. Nonetheless, we are still significantly behind the point where premium made for mobile content is mainstream, largely available, and affordable. Significant issues must be overcome. The small screen size is one of them. Indeed, the direct transfer of conventional contents (i.e. not specifically shot for mobile devices) will provide a video in which the main characters or objects of interest may become indistinguishable from the rest of the scene. Therefore, it is required to retarget the content. Different solutions exist, either based on distortion of the image, on removal of redundant areas, or cropping. The most efficient ones are based on dynamic adaptation of the cropping window. They significantly improve the viewing experience by zooming in the regions of interest. Currently, there is no common agreement on how to compare different solutions. A retargeting metric is proposed in order to gauge its quality. Eye-tracking experiments, zooming effect through coverage ratio and temporal consistency are introduced and discussed.
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References
Avidan, S., Shamir, A.: eam carving for content-aware image resizing. ACM Transactions on Graphics, SIGGRAPH 26 (2007)
Aziz, M.Z., Mertsching, B.: Fast and robust generation of feature maps for region-based visual attention. Image Processing 17(5), 633–644 (2008)
Chamaret, C., Le Meur, O.: Attention-based video reframing: validation using eye-tracking. In: ICPR (2008)
Chen, L., Xie, X., Fan, X., Ma, W., Zhang, H., Zhou, H.: A visual attention model for adapting images on small displays. ACM Multimedia Systems Journal 9(4) (2003)
Deselaers, T., Dreuw, P., Ney, H.: Pan, zoom, scan – time-coherent, trained automatic video cropping. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2008)
Fan, X., Xie, X., Ma, W., Zhang, H., Zhou, H.: Visual attention based image browsing on mobile devices. In: ICME 2003, vol. 1, pp. 53–56 (2003)
Itti, L., Koch, C.: Model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Le Meur, O., Le Callet, P., Barba, D.: Predicting visual fixations on video based on low-level visual features. Vision Research 47(19), 2483–2498 (2007)
Le Meur, O., Le Callet, P., Barba, D., Thoreau, D.: A coherent computational approach to model the bottom-up visual attention. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(5), 802–817 (2006)
Le Meur, O., Castellan, X., Le Callet, P., Barba, D.: Efficient Saliency-Based Repurposing Method. In: IEEE International Conference on Image Processing, pp. 421–424 (2006)
Liu, F., Gleicher, M.: Video retargeting: automating pan and scan. In: MULTIMEDIA 2006: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 241–250. ACM Press, New York (2006)
Liu, H., Xie, X., Ma, W., Zhang, H.: Automatic browsing of large pictures on mobile devices. In: ACM Multimedia Conference, pp. 148–155 (2003)
Kraehenbuehl, P., Manuel Lang, A.H., Gross, M.: A system for retargeting of streaming video. In: ACM Transactions on Graphics (Proc. of SIGGRAPH Asia) (2009)
Rubinstein, M., Shamir, A., Avidan, S.: Improved seam carving for video retargeting. ACM Transactions on Graphics (SIGGRAPH) 27(3), 1–9 (2008)
Santella, A., Agrawala, M., Decarlo, D., Salesin, D., Cohen, M.: Gaze-based interaction for semi-automatic photo cropping. In: Proceedings of ACM’s CHI 2006, pp. 771–780 (2006)
Seyler, A.J., Budrikis, Z.: Details perception after scene changes in television image presentations. IEEE Trans. Inform. Theory 11(1), 31–43 (1965)
Tao, C., Jia, J., Sun, H.: Active window oriented dynamic video retargeting. In: International Conference Computer Vision (2007)
Tatler, B.W., Baddeley, R.J., Gichrist, I.D.: Visual correlates of eye movements: Effects of scale and time. Vision Research 45(5), 643–659 (2005)
Wolf, L., Guttmann, M., Cohen-Or, D.: Non-homogeneous content-driven video-retargeting. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–6 (October 2007)
Zhang, G., Cheng, M., Hu, S., Martin, R.R.: A shape-preserving approach to image resizing. Pacific Graphics 28 (2009)
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Chamaret, C., Le Meur, O., Guillotel, P., Chevet, JC. (2012). How to Measure the Relevance of a Retargeting Approach?. In: Kutulakos, K.N. (eds) Trends and Topics in Computer Vision. ECCV 2010. Lecture Notes in Computer Science, vol 6554. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35740-4_13
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DOI: https://doi.org/10.1007/978-3-642-35740-4_13
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