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An adaptive interval coding method and decoding method based on slwe probability estimation model
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A technology of probability estimation and interval coding, which is applied in digital video signal modification, electrical components, image communication, etc., and can solve problems such as coding performance degradation
Active Publication Date: 2017-11-03
HARBIN INST OF TECH
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[0004] The present invention is to solve the problem that the coding performance of the existing interval coding is reduced under the environment where the characteristics of the information source are constantly changing, thereby proposing an adaptive interval coding method and a decoding method based on the SLWE probability estimation model
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specific Embodiment approach 1
[0047] Specific implementation mode 1. Combination figure 1 Describe this specific implementation mode, an adaptive interval coding method based on the SLWE probability estimation model,
[0048] Step 1. Statistical source basic information: read the data to be encoded in bytes, count the length of the data to be encoded BSIZE, the maximum symbol ma, the minimum symbol mi, and calculate the number of symbol types N=ma-mi+1, each The symbol types are represented by indexes 0,...,N-1 respectively;
[0049] Step 2. Initialization: For 32-bit systems, the upper bound of the initialization interval is: R ini=0xffffffff, the lower bound is: L ini =0x00000000, then the original range size is: Range=0xffffffff, the initial range length occupied by each symbol during initialization is: range[i]=Range / N, i=0,...,N-1, when the initialization range is normalized Critical Threshold: Range 0 = 0x00001000;
[0050] Step 3. Calculate the cumulative interval length based on the read chara...
specific Embodiment approach 2
[0068] Specific embodiment two, combine figure 2 Describe this specific embodiment, an adaptive interval decoding method based on the SLWE probability estimation model,
[0069] Step A1, read the basic information file of the information source, obtain the original data length BSIZE, the maximum value ma of the symbol, the minimum value mi of the symbol, and calculate the number of symbol types N=ma-mi+1;
[0070] Step A2, initialize R as in the initialization process in the coding process ini , L ini , initialize the initial interval length range[i] occupied by each symbol, i=0,...,N-1, read the code stream file in bytes, and obtain the initial identifier tag;
[0071] The specific method is: first initialize tag=0x00000000, then read the code stream file in bytes and perform an OR operation with the tag, and then shift the tag to the left by 8 bits, and the above process is carried out four times in a row;
[0072] Step A3, according to the tag and the lower bound of the...
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Abstract
An adaptive interval encoding method and a decoding method based on the SLWE probability estimation model relate to an adaptive interval encoding and decoding technology for information source probability estimation using the stochastic learning weak estimation theory (SLWE). It is to solve the problem of reduced coding performance of the existing interval coding in the environment where the characteristics of the signal source are constantly changing. The present invention mainly has two innovations: one is to use the idea of SLWE to design a source probability estimation model in interval coding, and to avoid the occurrence of interval degradation by setting the lower limit of the interval, while improving the coding efficiency; In the process of implementation, the probability update is replaced by the interval update, which avoids the rounding effect of the floating-point addition operation. The interval encoding method realized by the invention is suitable for encoding in the non-stationary environment of information source characteristics, and compared with the traditional interval encoding method based on the maximum likelihood idea for probability estimation, the encoding performance is improved by 2% to 10%.
Description
technical field [0001] The invention relates to an adaptive interval encoding and decoding technology for information source probability estimation by using stochastic learning weak estimation theory (SLWE). Background technique [0002] With the vigorous development of information industry and multimedia technology, the amount of data generated every day is getting larger and larger. In order to save storage space and transmission bandwidth, the development of various data compression technologies is urgently needed. Entropy coding is a kind of data compression technology, and it is also a key link in many international compression standards, occupying an important position in the field of data compression. Its theoretical basis is Shannon's information theory, which mainly achieves the purpose of compressing data by removing statistical redundancy in the data. The theoretical limit that can be compressed is the Shannon entropy of the data to be compressed. At present, com...
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