Encoding device, decoding device, encoding method, encoding program, decoding method, and decoding program

Abstract

A data predicted value generating unit generates a predicted value (data predicted value) for original data intended to be encoded, based on a history of original data which is floating-point data. A data predicted value modifying unit adjusts a mantissa value of the data predicted value by aligning an exponent value of the data predicted value with an exponent value of the original data. A first residual generating unit generates a residual (first residual) between new original data and the data predicted value after being adjusted. A first residual predicted value generating unit generates a predicted value for the first residual (first residual predicted value), based on a history of first residuals. A second residual generating unit generates a residual (second residual) between the first residual and the first residual predicted value. A residual encoding unit generates encoded data by encoding the second residual.

Description

technical field [0001] The present invention relates to, for example, an encoding device, a decoding device, an encoding method, an encoding program, a decoding method, and a decoding program for encoding (compressing) or decoding data. Background technique [0002] As an encoding method for losslessly compressing a data sequence, the following method is used. [0003] First, a predicted value for encoding target data is obtained (predicted value generation). Next, the residual between the predicted value and the encoding target data is obtained (residual generation). Then, the data sequence composed of residuals is encoded in an appropriate manner to obtain encoded data (residual encoding). [0004] When variable-length coding such as gamma coding is used in residual coding, coded data consisting of values ​​smaller than coding target data (values ​​close to 0) are often obtained by inserting a stage of calculating the residual, Improves compression through variable leng...

AI Technical Summary

Problems solved by technology

[0012] That is, since the magnitude of the median between the mantissa "1.23" of the encoding target data and the mantissa "9.98" of the predicted value is different, the comparison of these mantissas has no significance as a floating-point number, and the comparison of these mantissas cannot be achieved. to a value meaningful as the residual of the mantissa

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