Multimedia Technology

1. Multimedia Technology Soochow University Library Chen Jiacui Jane Chen-SUL

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

3. 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.

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

5. Outline • Multimedia Data Compression • Trends of multimedia technology

6. Multimedia Data Compression • Still Image Compression Standards • Basic Video Compression Techniques • MPEG Video Compression • MPEG audio standards

7. 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

8. 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

9. Setting compression parameters File save generates factors: 1.3, 2.45, 6.87

10. 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

11. 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.

12. 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)

13. 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.

14. 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 11544:1999. It defines the compression scheme not the file format.

15. 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.

16. 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 amountof compression that can be obtained.

17. 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.

18. 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.

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

20. 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).

21. 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.

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

23. 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)

24. 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.

25. 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.

26. 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/recognition; • 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.

27. 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.

28. 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.

29. 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.

30. Questions?

31. Thank all of you for your attendance!