Tuesday, November 10, 2009

Psychovisual Redundancy

PsychoVisual Redundancy

It is known that the human eye does not respond to all visual information with equal sensitivity. Some information is simply of less relative importance. This information is referred to as psychovisual redundant and can be eliminated without introducing any significant difference to the human eye. The reduction of redundant visual information has some practical applications in image compression. Since the reduction of psychovisual redundancy results in quantitative loss of information, this type of reduction is referred to as quantisation. The most common technique for quantisation is the reduction of number of colours used in the image., thus colour quantisation. Since some information is lost, the colour quantisation is an irreversible process. So the compression techniques that used such process are lossy. It should be noted that even if this method of compression is lossy, in situations where such compression technique is acceptable the compression can be very effective and reduce the size of the image considerably.

3 comments:

  1. can you please provide me an example....

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  2. Hi Keshang,
    Thank you for asking with an example.
    Human eye by nature has this psychovisual redundancy property. It means our human eyes are less sensitive to color information and more sensitive to black and white information. So if black and white information is removed, our human eye senses it clearly and so we should not remove black and white information from images or video as it our eyes are more sensitive, it means more visualizing capabilities towards this black and white information.This black and white information is also called brightness information present in the image or Luma component of the image.Similarly our eye is less sensitive to color information. This is due to the visual perception of our eye.So due to less sensitive to color of our eye, even if color information is removed, our eye doesn't perceive or recognize..so removing color information from the color image helps in image compression or video compression.

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