By Vasudev Bhaskaran
Image and Video Compression criteria: Algorithms and Architectures provides an advent to the algorithms and architectures that underpin the picture and video compression criteria, together with JPEG (compression of nonetheless images), H.261 (video teleconferencing), MPEG-1 and MPEG-2 (video garage and broadcasting). furthermore, the ebook covers the MPEG and Dolby AC-3 audio encoding criteria, in addition to rising strategies for photograph and video compression, comparable to these according to wavelets and vector quantization.
The publication emphasizes the rules of those criteria, i.e. suggestions reminiscent of predictive coding, transform-based coding, movement reimbursement, and entropy coding, in addition to how they're utilized in the factors. How each one normal is applied isn't really handled, yet the ebook does offer the entire fabric essential to comprehend the workings of every of the compression criteria, together with info that may be used to guage the potency of assorted software program and implementations conforming to the factors. specific emphasis is put on these algorithms and architectures that experience been came upon to be valuable in functional software program or undefined implementations.
Audience: A worthy reference for the graduate scholar, researcher or engineer. can also be used as a textual content for a path at the topic.
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Additional info for Image and Video Compression Standards: Algorithms and Architectures (The Springer International Series in Engineering and Computer Science)
16 CHAPTER 2 is defined as the self-information for symbol Sit that is. the infonnation we get from receiving 8i. If the base of the logarithm is two, then the self-information is measured in bits. If the base is ten, then self-infonnation is measured in nals (natural digits). For the remainder of this book, we always assume that information is measured in bits per symbol. 2) H(S) = log, - . LPi i Pi From information theory. if the symbols are distinct, then the average number of bits needed to encode them is always bounded by their entropy.
For example, typical images have sizes from 256 x 256 pixels to 64,000 x 64,000 pixels. One could view one instance of, say, the 256 x 256 image as a single message, 64,000 units long; however, it is very difficult to provide probability models for such long symbols. In practice. we typically view any image as a string of symbols. In the case of a 256 x 256 image, if we assume that each pixel takes values between zero and 255, then this image can be viewed as a sequence of symbols drawn from the alphabet 0, 1,2, ..
Is 16 bits with an arithmetic coder, 18 bits with a Huffman coder, and 7 x 3 = 21 bits with a fixed-length coder. Arithmetic coding yields better compression because it encodes a message as a whole new symbol instead of as separate symbols. 00 0 Si k 1 II w e r ? 7 Modified decoder table. 2 The Decoding Process The decoding process can be described using the same example. Let us assume that the message lluure? 0713348389. 7. Given the fractional representation of the input codeword value. the following algorithm outputs the corresponding decoded message.