Jpeg dct

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jpeg image compression technique discreate cosine transform

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Jpeg dct

  1. 1. DARSHAN KAREKAR 51026 Created for -POM
  2. 2. CONTENTS  What is JPEG?  Basic JPEG Compression Pipeline  JPEG examples  Major Coding Algorithms in JPEG  The Discrete Cosine Transform(DCT)  Significance / Where is this used?  Implementation Platform 10/6/2013 2JPEG-DCT(POM)
  3. 3.  JPEG: Joint Photographic Expert Group — an international  standard since 1992.  Works with colour and greyscale images  Up to 24 bit colour images (Unlike GIF)  Target photographic quality images (Unlike GIF)  Suitable for many applications e.g., satellite, medical, general,photography... What is JPEG? 10/6/2013 3JPEG-DCT(POM)
  4. 4. Continued..  JPEG standard is a collaboration among :  International Telecommunication Union (ITU)  International Organization for Standardization (ISO)  International Electrotechnical Commission (IEC)  The official names of JPEG :  Joint Photographic Experts Group  ISO/IEC 10918-1 Digital compression and coding of continuous-tone still image  ITU-T Recommendation T.81 10/6/2013 4JPEG-DCT(POM)
  5. 5. Basic JPEG Compression Pipeline  JPEG compression involves the following:  Encoding Decoding – Reverse the order for encoding 10/6/2013 5JPEG-DCT(POM)
  6. 6. JPEG examples  note that the two images on the left look identical  JPEG requires 6x less bits 10/6/2013 6JPEG-DCT(POM)
  7. 7. Major Coding Algorithms in JPEG  The Major Steps in JPEG Coding involve:  Colour Space Transform and subsampling (YIQ)  DCT (Discrete Cosine Transformation)  Quantisation  Zigzag Scan  DPCM on DC component  RLE on AC Components  Entropy Coding — Huffman or Arithmetic 10/6/2013 7JPEG-DCT(POM)
  8. 8. The Discrete Cosine Transform(DCT)  In the same family as the Fourier Transform  Converts data to frequency domain.  Represents data via summation of variable frequency cosine waves.  Since it is a discrete version, conducive to problems formatted for computer analysis.  Captures only real components of the function.  Discrete Sine Transform (DST) captures odd (imaginary) components → not as useful.  Discrete Fourier Transform (DFT) captures both odd and even components → computationally intense. 10/6/2013 8JPEG-DCT(POM)
  9. 9. Significance / Where is this used?  Image Processing  Compression - Ex.) JPEG  Scientific Analysis - Ex.) Radio Telescope Data  Audio Processing  Compression - Ex.) MPEG – Layer 3, aka. MP3  Scientific Computing / High Performance Computing (HPC)  Partial Differential Equation Solvers 10/6/2013 9JPEG-DCT(POM)
  10. 10. Significance Cont.  Image Processing Example  Exhibits Energy Compaction  Drop small amplitude coefficients Original Image DCT Transformed Image 10/6/2013 10JPEG-DCT(POM)
  11. 11. Implementation Platform NVIDIA CUDA Version 2.010/6/2013 11JPEG-DCT(POM)
  12. 12. EXAMPLE JPEG-DCT(POM) 12 0 1 2 3 4 5 6 7 01234567 u v The 8x8 DCT basis 10/6/2013
  13. 13. JPEG-DCT(POM) 13 Example : Y the luminance of an image W H 8x8 values of luminance 48 39 40 68 60 38 50 121 149 82 79 101 113 106 27 62 58 63 77 69 124 107 74 125 80 97 74 54 59 71 91 66 18 34 33 46 64 61 32 37 149 108 80 106 116 61 73 92 211 233 159 88 107 158 161 109 212 104 40 44 71 136 113 66 8x8 DCT coefficiences DCT 699.25 43.18 55.25 72.11 24.00 -25.51 11.21 -4.14 -129.78 -71.50 -70.26 -73.35 59.43 -24.02 22.61 -2.05 85.71 30.32 61.78 44.87 14.84 17.35 15.51 -13.19 -40.81 10.17 -17.53 -55.81 30.50 -2.28 -21.00 -1.26 -157.50 -49.39 13.27 -1.78 -8.75 22.47 -8.47 -9.23 92.49 -9.03 45.72 -48.13 -58.51 -9.01 -28.54 10.38 -53.09 -62.97 -3.49 -19.62 56.09 -2.25 -3.28 11.91 -20.54 -55.90 -20.59 -18.19 -26.58 -27.07 8.47 0.31 10/6/2013
  14. 14. 10/6/2013 14JPEG-DCT(POM)
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