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LOSSLESS PREDICTIVE
CODING
LOSSLESS PREDICTIVE CODING
 Does not require decomposition of an image into a
collection of bit planes.
 Based on eliminating the inter-pixel redundancies
closely spaced pixels by extracting and code only
the new information in each pixel.
- New information: the difference between the actual and
predicted value of that pixel.
 The coding system consists of an encoder and a decoder,
each contains an identical predictor (Fig. 8.19).
LOSSLESS CODING MODEL
 The predictor generates the anticipated value of that
pixel based on some number of past inputs.
 Form the difference or predictor en, which is coded
using a variable-length code to generate the next element
of the compressed data stream.
 Generate fn based on global, local and adaptive
 In 1D linear prediction f(x , y) is a function of the
previous pixels on the current line alone.
 In 2D predictive coding, the prediction is a function
of the previous pixel in a left -to-right, top-to-bottom
scan of an image.
 In 3D case, it is based on the pixels and the previous
pixels of preceding frames.
EXAMPLE
THANK YOU

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Lossless predictive coding in Digital Image Processing

  • 2. LOSSLESS PREDICTIVE CODING  Does not require decomposition of an image into a collection of bit planes.  Based on eliminating the inter-pixel redundancies closely spaced pixels by extracting and code only the new information in each pixel. - New information: the difference between the actual and predicted value of that pixel.  The coding system consists of an encoder and a decoder, each contains an identical predictor (Fig. 8.19).
  • 4.  The predictor generates the anticipated value of that pixel based on some number of past inputs.  Form the difference or predictor en, which is coded using a variable-length code to generate the next element of the compressed data stream.  Generate fn based on global, local and adaptive
  • 5.  In 1D linear prediction f(x , y) is a function of the previous pixels on the current line alone.  In 2D predictive coding, the prediction is a function of the previous pixel in a left -to-right, top-to-bottom scan of an image.  In 3D case, it is based on the pixels and the previous pixels of preceding frames.