Advances in visual data compression and communication by Wu F.

By Wu F.

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The theoretical basis of compression and transmission of visual data is Shannon’s information theory [4]. 1. A transmitter sends a source S over a noisy channel to a receiver. The channel adds a certain noise to the transmitted source. 1 The basic framework for source and channel coding. data with noise. To efficiently achieve it, there are two core technologies needed: source coding and channel coding. Source usually contains a certain redundancy and thus directly transmitting it would cost more channel bandwidth than what is really needed.

The distribution of symbols are not uniform. Therefore, a VLC (similar to Huffman coding) is designed for the symbols or an arithmetic code is selected. The sign bit of every nonzero coefficient is coded with the symbol. In addition to the compressed coefficients, motion vectors and some metadata (like sequence headers, frame headers, and block headers) are also compressed by VLC. All these parts form the final compressed stream. 2 Technical Evolution Although the framework of hybrid video coding has changed little in the past few decades, compression technologies of motion compensation, transform, quantization, and entropy coding have been continuously improving.

In the transmitter, source coding tries to remove the redundancy from the source when possible, by using various compression technologies. The output after source coding should be randomly distributed bits. Since the channel is noisy, the compressed bits will be corrupted by adding channel noise. Channel coding tries to introduce a certain redundancy back to the source, which protects the compressed bits. At the receiver, channel decoding corrects the received errors by using redundancy. Source decoding recovers the source from the compressed bits.

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