Comparative study of various still image coding techniques'

1
Comparative study of various still image coding
techniques.

• Harish Bhandiwad
• 1000579432
• EE5359 Multimedia Processing

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Why Do We Need Compression?

• Requirements may outstrip the anticipated
  increase of storage space and bandwidth 11
• For data storage and data transmission
• DVD
• Video conference
• Printer
• The bit rate of uncompressed digital cinema data
  exceeds 1 Gbps

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Image Compression(Bandwidth Compression vs. Bit
Rate Reduction)

• Reduction of the number of bits needed to
• represent a given image or its information
• Image compression exploits the fact that all
  images are not equally likely
• Exploits energy gaps in signal

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Lossless or Lossy Compression

• Lossless compression 12
• There is no information loss, and the image can
  be reconstructed exactly the same as the original
• Applications Medical imagery, Archiving
• Lossy compression 13
• Information loss is tolerable
• Many-to-1 mapping in compression eg. quantization
• Applications commercial distribution (DVD) and
  rate constrained environment where lossless
  methods cannot provide enough compression ratio

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Standards

• JPEG
• JPEG-LS
• JPEG2000
• JPEG-XR
• MPEG4-VTC
• AIC

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JPEG Encoder and Decoder 1
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• JPEG-Baseline
• 8x8 block based DCT
• Scalar quantization
• Zig-zag scanning
• Different quantization tables for luminance and
  chrominance components
• Huffman coding
• JPEG2000
• Relies on wavelet transform
• EBCOT scheme for coding wavelet coefficients
• Adaptive context-based binary arithmetic coding
• This project disables tiling and scalable mode
  for comparison as they adversely affect
  rate-distortion performance

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JPEG-LS

• JPEG-LS is ISO/ITU-T standard for lossless coding
  of still images
• based on adaptive prediction, context modeling
  and Golomb coding 2
• does not provide support for scalability, error
  resilience or any such functionality

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JPEG-XR encoder and decoder
Coded image
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JPEG-XR

• JPEG XR , a coded file format designed mainly for
  storage of continuous-tone photographic content
• supports wide range of color formats including
  n-channel encodings using fixed and floating
  point numerical representations, bit depth
  varieties
• Uses block-based image coder similar to
  traditional image-coding paradigm color
  conversion, transform, coefficient scanning,
  scalar quantization and entropy coding 14
• Uses lapped bi-orthogonal transform (LBT) as its
  decorrelation engine which supports both lossy
  and lossless compression 14

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MPEG-4 Visual Texture Coding

• Used in MPEG-4 standard to compress the texture
  information in photo realistic 3D models
• Based on the discrete wavelet transform (DWT),
  scalar quantization, zero-tree coding and
  arithmetic coding 7
• MPEG-4 VTC supports SNR scalability through the
  use of different quantization strategies single
  quantization, multiple quantization and bi-level
  quantization 7

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Advanced Image Coding
(a) Encoder 3
(b) Decoder 3
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Advanced Image Coding

• It is a still image compression system which is
  a combination of H.264 and JPEG standards 3.
• Features
• No sub-sampling- higher quality / compression
  ratios
• 9 prediction modes as in H.264
• Predicted blocks are predicted from previously
  decoded blocks
• Uses integer DCT to transform 8x8 residual block
  instead of transform coefficients as in JPEG
• Employs uniform quantization
• Uses floating point algorithm
• Coefficients transmitted in scan-line order
• Makes use of CABAC similar to H.264 with several
  contexts

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Evaluation Methodology for Comparison Image
Quality Measures

• Criteria used to evaluate compression quality
• Two types of quality measures
• Objective quality measure- PSNR, MSE
• Structural quality measure- SSIM
• MSE and PSNR for a NxM pixel image are defined as
• where x is the original image and y is the
  reconstructed image. M and N are the width and
  height of an image and L is the maximum pixel
  value in the NxM pixel image.

-----(1)
-----(2)
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Structural Similarity Method

• This method emphasizes that the Human Visual
  System (HVS) is highly adapted to extract
  structural information from visual scenes.
  Therefore, structural similarity measurement
  should provide a good approximation to perceptual
  image quality 8.
• The SSIM index is defined as a product of
  luminance, contrast and structural comparison
  functions 8.
• where gt 0, gt 0 and gt 0 are parameters
  used to adjust the relative importance of the
  three components
• where µ is the mean intensity, and s is the
  standard deviation as a round estimate of the
  signal contrast. C1 and C2 are constants. M is
  the numbers of samples in the quality map.

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References

• 1G. K. Wallace, The JPEG still picture
  compression standard, Communication of the ACM,
  vol. 34, pp. 31-44, April 1991
• 2 http//en.wikipedia.org/wiki/Lossless_JPEG
• 3 AIC website http//www.bilsen.com/aic/
• 4 P. Topiwala, Comparative study of JPEG2000
  and H.264/AVC FRExt I-frame coding on high
  definition video sequences, Proc. SPIE Intl
  Symposium, Digital Image Processing, Vol 5909,
  59090V, San Diego, Aug. 2005.
• 5 R. Veerla, Z. Zhang and K.R. Rao, Advanced
  image coding and its comparison with various
  still image codecs, 2008
• 6 T. Tran, L.Liu and P. Topiwala, Performance
  comparison of leading image codecs H.264/AVC
  intra, JPEG 2000, and Microsoft HD photo, Proc.
  SPIE Intl Symposium, Digital Image Processing,
  Vol. 6696, 66960B, San Diego, Sept. 2007
• 7 I. Moccagatta, H. Chen, MPEG-4 visual
  texture coding more than just compression,
  Rockwell Science Center. 1999
• 8 Z. Wang and A. C. Bovik, Image quality
  assessment from error visibility to structural
  similarity, IEEE Trans. Image Processing, vol.
  3, pp. 600 612, Apr. 2004
• 9 http//en.wikipedia.org/wiki/JPEG
• 10 http//en.wikipedia.org/wiki/JPEG_2000
• 11 http//en.wikipedia.org/wiki/Image_compressio
  n
• 12 http//en.wikipedia.org/wiki/Lossless_compres
  sion
• 13 http//en.wikipedia.org/wiki/Lossy_compressio
  n
• 14 http//en.wikipedia.org/wiki/HD_Photo
• 15 G. J. Sullivan, ISO/IEC 29199-2 (JpegDI
  part 2 JPEG XR image coding Specification),
  ISO/IEC JTC 1/SC 129/WG1 N 4492, Dec 2007