A Scheme for Attentional Video Compression

  • Rupesh Gupta
  • Santanu Chaudhury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

In this paper an improved, macroblock (MB) level, visual saliency algorithm, aimed at video compression, is presented. A Relevance Vector Machine (RVM) is trained over 3 dimensional feature vectors, pertaining to global, local and rarity measures of conspicuity, to yield probabalistic values which form the saliency map. These saliency values are used for non-uniform bit-allocation over video frames. A video compression architecture for propagation of saliency values, saving tremendous amount of computation, is also proposed.

Keywords

Salient Object Video Compression Visual Saliency Dimensional Feature Vector Saliency Propagation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Engelke, U., Maeder, A., Zepernick, H.J.: Analysing Inter-observer Saliency Variations in Task-Free Viewing of Natural Images. In: ICIP, pp. 1085–1088 (2010)Google Scholar
  2. 2.
    Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans. PAMI 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  3. 3.
    Huang, R., Sang, N., Liu, L., Tang, Q.: Saliency Based on Multi-scale Ratio of Dissimilarity. In: ICPR, pp. 13–16 (2010)Google Scholar
  4. 4.
    Hou, X., Zhang, L.: Saliency Detection: A Spectral Residual Approach. In: CVPR, pp. 1–8 (2007)Google Scholar
  5. 5.
    Guo, C., Zhang, L.: A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression. IEEE Trans. Image Proc. 19(1), 185–198 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Yu, Y., Wang, B., Zhang, L.: Pulse Discrete Cosine Transform for Saliency-Based Visual Attention. In: ICDL, pp. 1–6 (2009)Google Scholar
  7. 7.
    Chiang, J., Hsieh, C., Chang, G., Jou, F., Lie, W.: Region-of-Interest Based Rate Control Scheme with Flexible Quality on Demand. In: ICME, pp. 238–242 (2010)Google Scholar
  8. 8.
    Itti, L.: Automatic Foveation for Video Compression Using a Neurobiological Model of Visual Attention. IEEE Trans. Image Proc. 13(10), 1304–1318 (2004)CrossRefGoogle Scholar
  9. 9.
    Li, Z., Qin, S., Itti, L.: Visual Attention Guided Bit Allocation in Video Compression. Image and Vision Computing 29(1), 1–14 (2011)CrossRefGoogle Scholar
  10. 10.
    Liu, T., Sun, J., Zheng, N.-N., Tang, X., Shum, H.-Y.: Learning to Detect a Salient Object. In: CVPR, pp. 1–8 (2007)Google Scholar
  11. 11.
    Bruce, N.D.B., Tsotsos, J.K.: Saliency Based on Information Maximization. In: NIPS, pp. 155–162 (2006)Google Scholar
  12. 12.
    Harel, J., Koch, C., Perona, P.: Graph-Based Visual Saliency. In: NIPS, pp. 545–552 (2006)Google Scholar
  13. 13.
    Cernekova, Z., Pitas, I., Nikou, C.: Information Theory-Based Shot Cut/Fade Detection and Video Summarization. IEEE Trans. CSVT 16(1), 82–91 (2006)Google Scholar
  14. 14.
    Krulikovska, L., Pavlovic, J., Polec, J., Cernekova, Z.: Abrupt Cut Detection Based on Mutual Information and Motion Prediction. In: ELMAR, pp. 89–92 (2010)Google Scholar
  15. 15.
    Bhaskaran, V., Konstantinides, K.: Image and Video Compression Standards: Algorithms and Architectures. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  16. 16.
    Chen, Z., Lin, W., Ngan, K.N.: Perceptual Video Coding: Challenges and Approaches. In: ICME, pp. 784–789 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rupesh Gupta
    • 1
  • Santanu Chaudhury
    • 1
  1. 1.Dept. of EEIndian Institute of Technology DelhiIndia

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