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Transcript of IMAGE COMPRESSION - EVTEKusers.evtek.fi/~erkkir/ImageTechnology2013/1 ImagetechPDF/7... · Lossless...

  • 28.3.2012

    1

    IMAGE COMPRESSION

    Image Compression Why?

    Reducing transportation times Reducing file size

    A two way event - compression and decompression

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    Compression categories Compression = Image coding Still-image compression

    Compression of moving imageLossless Lossy

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    INTERFRAME and INTRAFRAME PROCESSING

    Interframe ProcessingPredictive Encoding

    Point to Point

    Line to Line

    Intraframe Processing

    Image compression meters Compress ratio =

    Original image size

    Compressed image size

    The larger the compression ratio, the smaller the result image

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    Image compression Compression method is not same as the image file-

    interchange format. Example TIFF -file format supports several compression methods

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    Why Can We Compress? Spatial redundancy

    Neighboring pixels are not independent but correlated

    Temporal redundancy

    Information vs Data

    REDUNDANTDATA

    INFORMATION

    DATA = INFORMATION + REDUNDANT DATA

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    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

    Image compression: symmetry

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    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

    Color imagecompression

    RED

    GREENBLUE

    SATURATION

    HUE

    BRIGHTNESS

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    Lossless image compression Image can be decompressed back to original Used when images future purpose of use is not known,

    example space exploration imagery is often studied for years following its origination

    Run-Length Coding

    76 76 76 76 76 78 79 79 80 80 80 98 98y

    76| 5 78| 1 79| 2 80| 3 98| 2

    Run-Length Codes(Brightness | Run-length)

    x

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    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

    Huffman CodingConverting the pixel brightness values in the original image to new variable-length codes, based on their frequency of occurrence in the image

    Arrange valuesin descending frequency of occurrence

    BrightnessHistogram

    Assign Huffmanvariable-lengthcodes

    Raw ImageData

    98,100,103,87,86,95...

    SubstituteHuffmancodes

    Appendcodelist

    Huffman CodeImage Data

    0,10,0,11001111,11011

    The flow of the Huffman coding operation.

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    Huffman coding

    character number Huffman code

    a (97) 45 1 1b (98) 23 1 0 01c (99) 2 0 0 000d (100) 1 0 1 0010CR (13) 1 1 1 00111LF (10) 1 0 00110

    2

    3

    5

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    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

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    Predictive Coding

    Based on the assumption that pixels 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)

    DPCM (Differential Pulse Code Modulation)

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    Block Coding Searching for repeated patterns (mostly in rows) Pixel patterns are put in Codebook Original images pixel pattern is replaced by codebook

    index in compressed image

    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

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    TRANSFORM CODING 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 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)

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    File formats and compression methods

    Standards crucial, so pictures are transportable between different systems

    Compression standards: CCITT group 3 and 4 (Fax-standard) Joint Bi-level Image Expert Group (JBIG) Joint Photographic Experts Group (JPEG)

    Motion picture: CCITT Recommendation H.261 H.264 Moving Picture Experts Group (MPEG)

    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.

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    Compression standards: JPEGJoint Photographic Experts Group (JPEG)

    One of the most important image data compression standards

    Developed for highly detailed gray-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 scheme number of

    retained frequency components can be changed to achieve different compression ratios

    DCT > Remove rare frequency components > DPCM > Huffman

    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.

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    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.

    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.

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    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.

    Original

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    JPEG

    27:1

    JPEG2000

    27:1

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    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 powerthan the decompression ones

    In addition to coding techniques used with JPEG, MPEG utilizes interframe coding methods

    MPEG-1 use CD-ROM and Internet MPEG-2 use DVD and Digi-TVMPEG-4 most advanced technology