By Wu F.
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Extra resources for Advances in visual data compression and communication
Huffman coding and arithmetic coding are taken as examples to explain what source coding is and how a source is compressed toward its entropy. The rate distortion theorem is also discussed because of its importance to guiding the lossy compression of a source. Second, we introduce the concept of channel capacity, which is the maximum rate that a channel can be used with negligible transmission errors, according to Shannon’s channel coding theorem. Hamming code is taken as an example to explain how channel coding works.
Video resolution has been increasing from early quarter common intermediate format (QCIF) to current high definition (HD). The increasing magnitude of visual data is also verified by wired and wireless network traffic. According to the Cisco visual networking index: forecast and methodology (2011–2016) , Internet video traffic will comprise 55% of all Internet traffic by 2016, rising from 51% in 2011. This does not include videos exchanged through peer-to-peer (P2P) file sharing. The sum of all forms of video, including television (TV), video on demand (VoD), Internet, and P2P, will be approximately 86% of all Internet traffic by 2016.
The theoretical basis of compression and transmission of visual data is Shannon’s information theory . 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.
Advances in visual data compression and communication by Wu F.
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