Can Human Perception Speed Up

Speed Up

Video Enhancement Frameworks?


  by V. Bruni, D.De Canditiis and D. Vitulano





      Because of masking , any kind of degradation on video sequences is not perceived in the same way in the whole image.

      For instance, noise is:

  • visible on flat regions;

  • negligible on textures.

  •   Two examples are shown in the Figures below:


    Cameraman with additive Gaussian noise. Left: Original Image. Right: noisy version.




    20-th frame of FlowerGarden with additive Gaussian noise. Left: Original Image. Right: noisy version.




      On the other hand, motion is not perceived in the same way in the whole sequence. It is:

  • visible on edges;

  • negligible on flat regions.




    The Proposed Strategy: SUVHEP

    (Speed Up of Video Enhancement based on Human Perception)


      A perception-based block classification can be then used:

      1-st Class: flat blocks - only denoising si required

      2-nd Class: edge blocks - motion estimation and denoising are required

      3-rd Class: textured blocks - no operations are required.



      A block scheme of the aforementioned classification is shown below:




      Starting from the classical SSIM [1,2]:



      one can get the two following hypothesis tests:


    1st Test




    2nd Test


    then the block is textured


      Apart from their simplicity, a confidence level can be proven for them as well as a very low complexity:




      operations per pixel, where N is the total number of pixels in the block B , while k is the number of its sub-blocks.

      Complexity can be further improved via a more effective version of SUVEHP: Fast SUVEHP (see [3] for details), that has the following complexity:







    Experimental Results


      Below there are three examples that show that the proposed classification though simple is still effective:

    FlowerGarden. Tennis. Coastguard.
    Classification map. Class 1,2 and 3 respectively are white, gray and black. Classification map. Classification map.
    The following results on some video sequences have been achieved by Fast SUVEHP:

    Mobile sequence, CoastGuard sequence, FlowerGarden sequence.

      The subjective quality of the restored sequences is unchanged or sometimes better with a considerable computational effort saving.

    But also the objective quality (in terms of SNR and SSIM) is even better with the proposed approach, as proven by the following plots:







    1. Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Trans. on Image Processing, 13, pp. 600-612, April 2004.

    2. Z. Wang, L. Lu, A. C. Bovik, Video Quality Assessment based on Structural Distortion Measurement, Signal Processing: Image Communication, 19(2), pp. 121-132, February, 2004.

    3. V. Bruni, D. De Canditiis, D. Vitulano, Speed-Up of Video Enhancement based on Human Perception, submitted to Signal Image and Video Processsing.