SC_IV_Entropy-Coding_v2_4

SC_IV_Entropy-Coding_v2_4 - Signal Compression 1 Copyright...

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Signal Compression 1 Copyright 2005-2008 Hayder Radha Entropy Coding Entropy coding is also known as zero-error coding, data compression or lossless compression. Entropy coding is widely used in virtually all popular international multimedia compression standards such as JPEG and MPEG. Signal Compression 2 Copyright 2005-2008 Hayder Radha A complete entropy codec, which is an encoder/decoder pair, consists of the process of encoding or compressing a random source (typically quantized transform coefficients) and the process of decoding or decompressing the compressed signal to perfectly regenerate the original random source. In other words, Signal Compression 3 Copyright 2005-2008 Hayder Radha there is no loss of information due to the process of entropy coding. Thus, entropy coding does not introduce any distortion, and hence, the combination of the entropy encoder and entropy decoder faithfully reconstructs the input to the entropy encoder. Signal Compression 4 Copyright 2005-2008 Hayder Radha Entropy Encoding Random Source Compressed Source Entropy Decoding Random Source Compressed Source Signal Compression 5 Copyright 2005-2008 Hayder Radha Therefore, any possible loss-of-information or distortion that may be introduced in a signal compression system is not due to entropy encoding/decoding. As we discussed previously, a typical image compression system, for example, includes a transform process, a quantization process, and an entropy coding stage. In such system, the distortion is introduced due to quantization. Moreover, Signal Compression 6 Copyright 2005-2008 Hayder Radha for such a system, and from the perspective of the entropy encoder, the input random source to that encoder is the quantized transform coefficients. Transform Quantization Entropy Coding Random Source Examples KLT DCT Wavelets Transform Coefficients Quantized Coefficients Compressed Source Examples Huffman Arithmetic Signal Compression 7 Copyright 2005-2008 Hayder Radha Code Design and Notations In general, entropy coding (or source coding) is achieved by designing a code , C , which provides a one- to-one mapping from any possible outcome a random variable X (source) to a codeword . There two alphabets in this case; one alphabet is the traditional alphabet of the random source X , and the Signal Compression 8 Copyright 2005-2008 Hayder Radha second alphabet ! is the one that is used for constructing the codewords. Based on the second alphabet ! , we can construct and define the set * D , which is the set of all finite-length string of symbols withdrawn from the alphabet ! . S i g n a l C o m p r e s s i o n 9 C o p y r i g h t 2 5- 2 8 H a y d e r R a d h a T h e m o s t c o m m o n a n d p o p u l a r c o d e s a r e b i n a r y c o d e s , w h e r e t h e a l p h a b e t o f t h e c o d e w o r d s i s s i m p l y t h e b i n a r y b i t s...
View Full Document

Page1 / 32

SC_IV_Entropy-Coding_v2_4 - Signal Compression 1 Copyright...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online