CIS679: Multimedia Basics

Multimedia data type Basic compression techniques

Multimedia Data Type

Audio Image Video

Audio

Digitization Sampling Quantization Coding

Higher sampling rate -> higher quality Nyquist sampling theorem: for lossless digitization, the

sampling rate should be at least twice the maximum frequency responses

Higher bits per sample -> higher quality Sampling at 8 KHz, 8 bit samples -> 64kbits/sec CD-quality audio

Sampling at 44.1KHz, 16 bit samples -> 705.6 kbits/sec

Image/Video

Digitization Scan a picture frame Digitize every pixel

Color represented by RGB Normally converted to Y (black and white TV),

U and V Luminance Y = 0.30R + 0.59G + 0.11 R Chrominance U = (B-Y) * 0.493 V = (R-Y) * 0.877

Video Transmission Standards

NTSC Y = 0.30R + 0.59G + 0.14B I = 0.60R + 0.28G + 0.32B Q = 0.21R + 0.52G + 0.21B

PAL

Studio-quality TV

NTSC 525 lines at 30 frames/second Y sampled at 13.5 MHz, Chrominance values at 6.75

MHz With 8-bit samples, Data rate = (13.5 + 6.75 + 6.75) * 8 = 216 Mbps

Summary of Multimedia Data Types

Audio data rate = 64kbps, and 705.6kbps Video date rate = 216 Mbps Compression is required!

Can Multimedia Data Be Compressed?

Redundancy can be exploited to do compression!

Spatial redundancy correlation between neighboring pixels in

image/video

Spectral redundancy correlation among colors

Psycho-visual redundancy Perceptual properties of human visual system

Categories of Compression

Lossless No distortion of the original content Used for computer data, medical images, etc.

Lossy Some distortion Suited for audio and video

Compression TechniquesRun-length Coding

EntropyEncoding

Huflfman Coding

Arithmetic Coding

DPCM

Prediction DM

FFT

Transformation DCT

Source Coding Bit Position

Layered Coding Subsampling

Sub-band Coding

Vector Quantization

J PEG

MPEG

Hybrid Coding H.261

DVI RTV, DVI PLV

Entropy Encoding Techniques

Lossless compression Run-length encoding

Represent stream as (c1, l1), (c2, l2),…, (ck, lk) 1111111111333332222444444 = (1, 10) (3, 5) (2,4)

(4, 5) Or ABCCCCCCCCDEFGGG = ABC!8DEFGGG

Pattern Substitution Substitute smaller symbols for frequently used

patterns

Huffman Coding

Use variable length codes Most frequently used symbols coded with

fewest bits Codes are stored in a codebook Codebook transferred with the compressed

stream

Source Encoding Techniques

Transformation encoding Transform the bit-stream into another domain Data in the new domain more amenable to

compression Type of transformation depends on data

Image/video transformed from time domain into frequency domain (DCT)

Differential/Predictive Encoding

Encoding the difference between actual value and a prediction of that value

Number of Techniques Differential Pulse Code Modulation (DPCM) Delta Modulation (DM) Adaptive Pulse Code Modulation (APCM)

How they work? When consecutive change little Suited for audio and video

Vector Quantization

Divide the data stream into blocks or vectors One or two dimensional blocks

Use codebooks Find the closest symbol in codebook for a

given sample Transmit the reference to that symbol Codebook present at sender/receiver When no exact match, could send the error

Lossy or lossless Useful with known signal characteristics Construct codebooks that can match a wide

range of symbols

Major Steps of Compression

Preparation Uncompressed analog signal -> sampled digital form

Processing Source coding DCT typically used: Transform from time domain ->

frequency domain

Quantization Quantize weights into integer codes Could use different number of bits per coefficient

Entropy encoding Lossless encoding for further compression

Conclusion

Multimedia data types Why multimedia can be compressed? Categories of compression Compression techniques

Entropy encoding Source encoding Hybrid coding

Major steps of compression What’s next?

JPEG MPEG