rm compression er

7/29/2019 RM Compression ER

1/15

Redes Para Multimedios

Prof. Ernesto Garca

Tcnicas de Compresin

para Multimedia

21 de Agosto de 2013


2/15

Audio

Digitization Sampling Quantization

Coding Higher sampling rate -> higher quality 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 -> 705.6kbits/sec


3/15

Image/Video Digitization

Scan a picture frame Digitize every pixel

Color represented by RGB Normally converted to Y, U and V Luminance Y = 0.30R + 0.59G + 0.11 R Chrominance U = (B-Y) * 0.493

V = (R-Y) * 0.877


4/15

8/26/2013

4

Compression Requirements Storage Requirements

Uncompressed audio: 8kHz, 8-bit quantization implies64Kbits to store per second

CD quality audio: 44.1 kHz, 16-bit quantization implies tostore 705.6 Kbits per second

PAL video format: 640x480 pixels, 24-bit quantization, 25fps implies to store 184 Mbps

NTSC at 30 frames/second with 8-bit samples, implies tostore 216 Mbps

Compression is required !


5/15

Requirement on Network Bandwidth / Bit-rate

5


6/15

General Data Compression SchemeEncoder

(compression)

Storage orNetworks

Decoder(decompression)

Input Data

Output Data

Codes /Codewords

Codes /Codewords

B0 = # bits required beforecompression

B1 = # bits required aftercompression

Compression Ratio = B 0 / B 1.


7/15

Compression Techniques


8/15

Compression Techniques Entropy Coding

Semantics of the information to encoded are ignored Lossless compression technique Can be used for different media regardless of their

characteristics

Source Coding Takes into account the semantics of the information to beencoded.

Often it uses lossy compression technique Hybrid Coding

Combine entropy coding with source coding Most multimedia compression algorithms are hybrid

techniques Examples: JPEG, JPEG-2000, H. 264, MPEG-2, MPEG-

4, MPEG-7, MPEG-21


9/15

Entropy Encoding Techniques Run-length encoding

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

(4, 5) Huffman Encoding

Statistical encoding Depends on occurrence frequency of single

characters or sequences of data bytes To determine Huffman code, it is useful to

construct a binary tree Leaves are characters to be encoded Nodes carry occurrence probabilities of the

characters belonging to the subtree


10/15

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 domaininto frequency domain (DCT)


11/15

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)


12/15

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


13/15

Spatial (intra-frame) compression: Compresses each frame in isolation (by separated),

treating it as a bitmapped image. Based on quantization of DCT coefficients.

Temporal (inter-frame) compression: Compresses sequences of frames by only storing

differences between them. Record displacement of object plus changed pixels

in area exposed by its movement. Based on Motion Compensation (MC).

Video Compression


14/15

Image compression applied to each frame.

Can therefore be lossless or lossy, but losslessrarely produces sufficiently high compression

ratios for volume of data.

Spatial Compression


15/15

Key frames are spatially compressed only Key frames often regularly spaced

(e.g., every 10 frames).

Difference frames only store the differences between the frame and the preceding frame or mostrecent key frame.Difference frames can be efficiently spatially compressed.

Temporal Compression

rm compression er

Documents