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Image compression

Image compression. Image Compression. Why? Reducing transportation times Reducing file size A two way event - compression and decompression. Compression categories. Compression = Image coding Still-image compression Compression of moving image. Point to Point. Interframe Processing.

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Image compression

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  1. Image compression

  2. Image Compression • Why? • Reducing transportation times • Reducing file size • A two way event - compression and decompression

  3. Compression categories • Compression = Image coding • Still-image compression • Compression of moving image

  4. Point to Point Interframe Processing Predictive Encoding Line to Line INTERFRAME and INTRAFRAME PROCESSING Intraframe Processing

  5. Image compression meters • Compress ratio = Original image size Compressed image size • The larger the compression ratio, the smaller the result image

  6. Image compression • Compression method is not same as the image file-interchange format. • Example TIFF -file format supports several compression methods

  7. Why Can We Compress? • Spatial redundancy • Neighboring pixels are not independent but correlated • Temporal redundancy

  8. Information vs Data REDUNDANTDATA INFORMATION DATA = INFORMATION + REDUNDANT DATA

  9. Image compression fundamentals • Same compression method is not to be used more than once. • But you can use different methods at the same time, especially different lossless methods like LZW and PKZIP

  10. Image compression: symmetry

  11. Color image compression • RGB - apply the same compression scheme to the three color component images • Convert the image from the RGB color space to a less redundant space, because RGB components carries a lot of same information. • RGB --> HSB, when Hue and Saturation components are well compressed

  12. RED GREEN BLUE Color imagecompression HUE BRIGHTNESS SATURATION

  13. Lossless image compression • Image can be decompressed back to original • Used when image’s future purpose of use is not known, example space exploration imagery is often studied for years following its origination

  14. 78| 1 79| 2 80| 3 98| 2 76| 5 x y 76 76 76 76 76 78 79 79 80 80 80 98 98 Run-Length Codes (Brightness | Run-length) Run-Length Coding

  15. Run-length coding • Codes the nearby pixels which has same brightness values in two values - Run-Length, RLE and brightness value • Error sensitive • Data explosion • Data errors

  16. Huffman or Entropy Coding • Converting the pixel brightness values in the original image to new variable-length codes, based on their frequency of occurrence in the image Arrange values in descending frequency of occurrence Assign Huffman variable-length codes Brightness Histogram Huffman Code Image Data Raw Image Data Substitute Huffman codes Append code list 0,10,0,1100 1111,11011 98,100,103, 87,86,95... The flow of the Huffman coding operation.

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

  18. Predictive Coding • Based on the assumption that pixel’s brightness can be predicted based on the brightness of the preceding pixel • Codes only the brightness value of the pixel next to each other • DPCM (Differential Pulse Code Modulation)

  19. DPCM (Differential Pulse Code Modulation)

  20. Block Coding • Searching for repeated patterns (mostly in rows) • Pixel patterns are put in Codebook • Original image’s pixel pattern is replaced by codebook index in compressed image

  21. Block Coding • LZW- compression (Lempel-Ziv-Welch) • Compression ratio 2:1 - 3:1 • Starting with a 256 single-pixel long codebook -> adding until it reaches its maximum length • LZW+Huffmann, where most common pixel patterns get shortest codes

  22. • Transform Coding - transform image - code the coefficients of the transform - transmit them - reconstruct by inverse transform • Benefits - transform coeff. relatively uncorrelated - energy is highly compacted - reasonable robust relative to channel errors TRANSFORM CODING

  23. Transform Coding • A form of lossy block coding, but it does not use codebook • Frequency domain • Frequency transformation finds the essential data in the image and coding is accurate • 8*8 pixel blocks • Discrete Cosine Transform (DCT)

  24. Why Do We Need International Standards? • International standardization is conducted to achieve inter-operability . • Only syntax and decoder are specified. • Encoder is not standardized and its optimization is left to the manufacturer. • Standards provide state-of-the-art technology that is developed by a group of experts in the field. • Not only solve current problems, but also anticipate the future application requirements.

  25. Compression standards: JPEG • Joint Photographic Experts Group (JPEG) • One of the most important image data compression standards • Developed for highly detailedgray-scale and color images / photographs • Most commonly used as a lossy image compression method, but lossless modes exist as well • JPEG uses several cascaded compression modes • Adjustable compression schemeà number of retained frequency components can be changed to achieve different compression ratios • DCT > Remove rare frequency components > DPCM/RLE > Huffman

  26. JPEG(Intraframe coding) • First generation JPEG uses DCT+Run length Huffman entropy coding. • Second generation JPEG (JPEG2000) uses wavelet transform + bit plane coding + Arithmetic entropy coding.

  27. Why DCT Not DFT? • DCT is similar to DFT, but can provide a better approximation with fewer coefficients • The coefficients of DCT are real valued instead of complex valued in DFT.

  28. The 64 (8 X 8) DCT Basis Functions • Each 8x8 block can be looked at as a weighted sum of these basis functions. • The process of 2D DCT is also the process of finding those weights.

  29. Zig-zag Scan DCT Blocks • Why? -- To group low frequency coefficients in top of vector. • Maps 8 x 8 to a 1 x 64 vector.

  30. Original

  31. JPEG 27:1

  32. JPEG2000 27:1

  33. Motion compression standards • Moving Picture Experts Group (MPEG) • Intended for the mass distribution of motion video sequences • Compression-asymmetric = compression techniques require more processing time and computing power than the decompression ones • In addition to coding techniques used with JPEG, MPEG utilizes interframe coding methods • MPEG-1 use CD-ROM and Internet • MPEG-2use DVD and Digi-TV • MPEG-4 most advanced technology

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