Multimedia Technology

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Multimedia Technology

• Soochow University Library
• Chen Jiacui

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Multimedia

• Multimedia is the term used to describe two or
  more types of media combined into a single
  packageusually denoting a combination of some or
  all of the following video, sound, animation,
  text, and pictures.

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Multimedia Technology

• And with the advent of multimedia, the computer
  has evolved into a distinctive medium that is
  uniquely capable of juxtaposing text, images,
  audio, and video.

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Business Process Convergence

• Technology convergence
• Devices, networks, services and multimedia
  content (movies, TV, gaming, music).
• Business convergence
• Business models, pricing simplicity,
  billing, payment, etc.

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Outline

• Multimedia Data Compression
• Trends of multimedia technology

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Multimedia Data Compression

• Still Image Compression Standards
• Basic Video Compression Techniques
• MPEG Video Compression
• MPEG audio standards

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Still Image Compression Standards

• JPEG Joint Photographic Experts Group
• Lossy compression of still images
• Lossless compression of still images
• JBIG Joint Bilevel Image Group
• GIF Graphics Interchange Format
• PNG Portable Network Graphics

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JPEG

• JPEG is a standardized image compression
  mechanism. It is designed for compressing either
  full-color or gray-scale images of natural,
  real-world scenes. It works well on photographs,
  naturalistic artwork, and similar material not
  so well on lettering, simple cartoons, or line
  drawings. JPEG handles only still images.
• Important Properties
• Setting compression parameters
• Trade off decoding speed against image quality

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Setting compression parameters
File save generates factors 1.3, 2.45, 6.87
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Image accuracy
Quality level 90 File size 10,582 bytes
Quality level 1 File size 923 bytes
Quality level 50 File size 5,154 bytes
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Lossy compression

• JPEG is "lossy compression", in which some amount
  of data is lost. Lossy compression technologies
  attempt to eliminate redundant or unnecessary
  information. Most video compression technologies
  use a lossy technique.

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JPEG JPEG 2000

• JPEG 2000 improved the following deficiencies
• Poor subjective performance at rates below
  0.25bits per pixel (bpp)
• Lack of ability to provide lossy and lossless
  compression in the same codestream
• Lack of robustness to bit errors in the
  compressed image
• Poor performance with computer-generated imagery
• Poor performance with compound documents (text
  and image)

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JPEG JPEG-LS

• JPEG-LS provides lossless and near lossless modes
  of operation
• The near lossless mode allow users to specify a
  bound (referred to as NEAR) on the error
  introduced by the compression algorithm.
• JPEG-LS exploits local structure and repetitive
  context within images to achieve efficient
  lossless and near lossless compression.

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JBIG

• JBIG (Joint Bi-level Image Group) is an advanced
  compression scheme utilizing lossless, predictive
  methods. The JBIG compression algorithm is
  defined by ISO/IEC S 115441999. It defines the
  compression scheme not the file format.

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The main characteristic of JBIG

• Compatible progressive/sequential coding. This
  means progressively coded image can be decoded
  sequentially and the other way around
• JBIG will be a lossless image compression all
  bits in the image before and after the
  compression and decompression will be exactly the
  same.

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JBIG2

• The JBIG2 provides a highly effective method for
  lossless compression of a generic bilevel image
• JBIG2 is the improved version of JBIG
• The JBIG2 takes advantage of the properties of
  the source material.
• It gives the user option of using lossy
  compression, which increase the amount of
  compression that can be obtained.

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GIF

• A standard defining a mechanism for the storage
  and transmission of raster-based graphics
  information.
• The GIF method is lossless it reproduces exactly
  the pixels that were in the original image upon
  viewing.

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GIF

• GIF is better than JPG for images with only a few
  distinct colors, such as line drawings, black and
  white images and small text that is only a few
  pixels high. With an animation editor, GIF images
  can be put together for animated images.
• GIF also supports transparency, where the
  background color can be set to transparent in
  order to let the color on the underlying Web page
  to show through.
• The compression algorithm used in the GIF format
  is owned by Unisys, and companies that use the
  algorithm are supposed to license the use from
  Unisys.

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PNG

• A file format for bitmapped graphic images,
  designed to be a replacement for the GIF format,
  without the legal restrictions associated with
  GIF.

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Basic Video Compression Techniques

• H.261
• H.261 is an earlier digital video compression
  standard, its principle of MC-based compression
  is retained in all later video compression
  standards. The standard was designed for
  videophone, videoconferencing and other
  audiovisual services over ISDN.
• H.263
• H.263 is an improved video coding standard for
  videoconferencing and other audiovisual services
  transmitted on Public Switched Telephone Networks
  (PSTN).

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MPEG

• MPEG is used for coding audio-visual information
  in a digital compressed format. The MPEG family
  of standards includes MPEG-1, MPEG-2 , MPEG-4,
  MPEG-7, and MPEG-21.

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MPEG

• Digital television set-top boxes
• DSS
• HDTV decoders
• DVD players
• Video conferencing
• Internet video
• Other applications

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MPEG

• MPEG-1 (Video CDs
• MPEG-2 (DVD, Digital TV)
• MPEG-4 (All Inclusive and Interactive)
• MPEG-7 (Meta-Data)
• MPEG-21 (Digital Rights Infrastructure)

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MPEG audio standards

• Layer 1
• Lossless compression of subbands optional
  simple masking model
• Layer 2
• More advanced masking model.
• Layer 3
• Additional processing for lower bit rates.

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Trends of multimedia technology

• Multimedia content analysis, processing, and
  retrieval
• Multimedia networking and systems support
• Multimedia tools, end-systems, and applications
  and
• Foundational sciences of multimedia.

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Content Analysis

• Image, audio and video characterization (feature
  extraction)
• Fusion of text, image, video and audio data
• Semantic image/video/audio classification
• Multimedia semantics modeling
• Image, video and audio similarity measures
• Unconstrained object and face detection/recognitio
  n
• Low- and high-level temporal video segmentation
• Benchmarking of content analysis methods and
  algorithms
• Generic methods and algorithms for content
  analysis and semantics modeling
• Affective content analysis.

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Multimedia Content Processing

• Speech Processing and Recognition
• Audio Enhancement
• Restoration and Analysis
• Image Representation and Modelling
• Image Restoration and Enhancement
• Colour Vision, 3D Vision, Image and Video
  Analysis
• Pattern Recognition
• Watermarking
• New Media.

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Content Search/Browsing/Retrieval

• Multimedia mining
• Active learning and relevance feedback
  techniques
• Query models, paradigms, and languages for
  multimedia content retrieval
• Browsing and visualization of multimedia data
  sets
• User interfaces for multimedia databases
• Search issues in distributed and heterogeneous
  systems, meta-search engines
• Benchmarking of search, browsing and retrieval
  methods and algorithms
• Generation of video summaries and abstractions.

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Content Management and Delivery

• Multimedia databases
• Efficient peer-to-peer storage and search
  techniques
• Indexing and data organization
• System optimization for search and retrieval
• Storage hierarchy, scalable storage.

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Questions?
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Thank all of you for your attendance!