Image processing : Compression

Image compression comprised of

• encoder

• decoder

• frames

• both encoders and decoders

Compression is done for saving

• storage

• bandwidth

• money

• storage and bandwidth

An alphabet consist of the letters A, B, C and D. The probability of occurrence is

P(A) = 0.4, P(B) = 0.1, P(C) = 0.2 and P(D) = 0.3. The Huffman code is

• A = 0

  B = 11

  C = 10

  D = 111

• A = 0

  B = 111

  C = 110

  D = 10

• A = 01

  B = 111

  C = 110

  D = 10

• A = 0

  B = 111

  C = 11

  D = 101

The basic idea behind Huffman coding is to

• compress data by using fewer bits to encode more frequently occurring characters

• expand data by using fewer bits to encode more frequently occurring characters

• compress data by using more bits to encode more frequently occurring characters

• compress data by using fewer bits to encode fewer frequently occurring characters

Huffman coding is an encoding algorithm used for

• lossy data compression

• broadband systems

• lossless data compression

• files greater than 1 Mbit

A Huffman encoder takes a set of characters with fixed length and produces a set of characters of

• fixed length

• random length

• variable length

• constant length

Segmentation is usually not perfect due to number of factors such as

• Noise, Bad illumination

• Object Contain several region

• Due to boundary-filling

• Due to closed contour

Criteria for region segmentation if the neighborhood pixels share the same features

• Pixels may be assigned to the same region

• Pixels may be assigned to the different region

Pixels are allocated to categories according to the range of values in which a pixel lies is called

• Thresholding based segmentation

• Edge- based segmentation

• Region based segmentation

• None of the above

Laplacian is a

• First order derivative filter

• Sobel operator

• Canny operator

• Second order derivative filter