09_CM0340_Basic_Compression_Algorithms

09_CM0340_Basic_Compression_Algorithms - Compression: Basic...

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353 JJ II J I Back Close Compression: Basic Algorithms Recap: The Need for Compression Raw Video, Image and Audio files can be very large: Uncompressed Audio 1 minute of Audio: Audio Type 44.1 KHz 22.05 KHz 11.025 KHz 16 Bit Stereo 10.1 Mb 5.05 Mb 2.52 Mb 16 Bit Mono 5.05 Mb 2.52 Mb 1.26 Mb 8 Bit Mono 2.52 Mb 1.26 Mb 630 Kb Uncompressed Images: Image Type File Size 512 x 512 Monochrome 0.25 Mb 512 x 512 8-bit colour image 0.25 Mb 512 x 512 24-bit colour image 0.75 Mb
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354 JJ II J I Back Close Video Can also involve: Stream of audio plus video imagery. Raw Video – Uncompressed Image Frames, 512x512 True Colour, 25 fps, 1125 MB Per Min HDTV — Gigabytes per minute uncompressed ( 1920 × 1080 , true colour, 25fps: 8.7GB per min) Relying on higher bandwidths is not a good option — M25 Syndrome. Compression HAS TO BE part of the representation of audio, image and video formats.
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355 JJ II J I Back Close Classifying Compression Algorithms What is Compression? E.g. : Compression ASCII Characters EIEIO E ( 69 ) z }| { 01000101 I ( 73 ) z }| { 01001001 E ( 69 ) z }| { 01000101 I ( 73 ) z }| { 01001001 O ( 79 ) z }| { 01001111 = 5 × 8 = 40 bits The Main aim of Data Compression is find a way to use less bits per character, E.g.: E ( 2bits ) z}|{ xx I ( 2bits ) z}|{ yy E ( 2bits ) z}|{ xx I2bits ) z}|{ yy O ( 3bits ) z}|{ zzz = 2 × E z }| { (2 × 2) + 2 × I z }| { (2 × 2) + O z}|{ 3 = 11 bits Note: We usually consider character sequences here for simplicity. Other token streams can be used — e.g. Vectorised Image Blocks, Binary Streams.
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356 JJ II J I Back Close Compression in Multimedia Data Compression basically employs redundancy in the data: Temporal — in 1D data, 1D signals, Audio etc. Spatial — correlation between neighbouring pixels or data items Spectral — correlation between colour or luminescence components. This uses the frequency domain to exploit relationships between frequency of change in data. Psycho-visual — exploit perceptual properties of the human visual system.
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357 JJ II J I Back Close Lossless v Lossy Compression Compression can be categorised in two broad ways: Lossless Compression — after decompression gives an exact copy of the original data Examples : Entropy Encoding Schemes (Shannon-Fano, Huffman coding), arithmetic coding,LZW algorithm used in GIF image file format. Lossy Compression — after decompression gives ideally a ‘close’ approximation of the original data, in many cases perceptually lossless but a byte-by-byte comparision of files shows differences. Examples : Transform Coding — FFT/DCT based quantisation used in JPEG/MPEG differential encoding, vector quantisation
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358 JJ II J I Back Close Why do we need Lossy Compression? Lossy methods for
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This note was uploaded on 01/26/2012 for the course CM 0340 taught by Professor Davidmarshall during the Fall '09 term at Cardiff University.

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09_CM0340_Basic_Compression_Algorithms - Compression: Basic...

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