By Vasudev Bhaskaran
ISBN-10: 147572358X
ISBN-13: 9781475723588
ISBN-10: 1475723601
ISBN-13: 9781475723601
Image and Video Compression criteria: Algorithms and Architectures offers an creation 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 booklet covers the MPEG and Dolby AC-3 audio encoding criteria, in addition to rising suggestions for snapshot and video compression, equivalent to these in keeping with wavelets and vector quantization.
The publication emphasizes the principles of those criteria, i.e. strategies comparable to predictive coding, transform-based coding, movement repayment, and entropy coding, in addition to how they're utilized within the criteria. How each one regular is applied isn't handled, however the e-book does offer the entire fabric essential to comprehend the workings of every of the compression criteria, together with info that may be used to judge the potency of assorted software program and implementations conforming to the factors. specific emphasis is put on these algorithms and architectures which have been came upon to be necessary in useful software program or implementations.
Audience: A worthy reference for the graduate pupil, researcher or engineer. can also be used as a textual content for a direction at the subject.
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Extra info for Image and Video Compression Standards: Algorithms and Architectures
Sample text
We illustrate the code construction process with an example. Example 1: Let S = 80,82, ... 1. Here, the Huffman code table design is based on the approach described in the previous section. 00040 2 2 3 3 4 4 5 5 6 6 7 7 7 8 9 9 Codeword 11 10 011 010 0011 0010 00011 00010 000011 000010 0000011 0000010 0000001 00000001 000000001 000000000 Unconstrained length Huffman codewords. symbol. Note that, the longest codeword is nine bits. Thus, a 512-entry table is needed for lookup-table-based decoding.
Until symbol decoded is ? End We show a few steps ofthe decoding process in our example. 1. Initially, Previousl ow = 0, PreviOUShigh = 1, and Range = 1. For i = 6 , cumprob 6 < < cumprob. Thus _ value-Previouslow Range 5 , the first decoded symbol is l. 2, respectively. 2. 05 < cumprob5 . Thus, the second decoded symbol is l. 04, respectively. 3. Repeating the decoding process yields that i = 5 satisfies the limits cumprob 5 :S val~~o2·06 < cumprob 4 . Thus, the third decoded symbol is u. 004, respectively.
8 N will be leaf nodes of this tree. The codeword for each symbol 8i is obtained by traversing the binary tree from its root to the leaf node corresponding to 8i. 3. The input data to be compressed is composed of symbols in the alphabet k, l, U, w, e, r, ? In Step 1, we sort the probabilities. In Step 2, we merge the two symbols k and W to form the new symbol (k, w). The probability of occurrence for the new symbol is the sum of the probabilities of occurrence for k and w. 3. We repeat this process through Step 6.