|Budget Amount *help
¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 2002: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2001: ¥1,400,000 (Direct Cost: ¥1,400,000)
The lossless data compression is a fundamental subject both in information and communication theory. The model-based method is a major approach in the subject, and there source estimation is combined with the arithmetic coding. A most popular source model is the so called tree model, and for this the context tree weighting (CTW) method is an ideal estimation method. The CTW method can be modified to be based on a finite window it can follow up to a change of source probability. A crucial argument here is the computability of a sub-probability estimator, which enables the usage of arithmetic coder. A yielded estimator, we have discovered, is called FWCTW (finite window CTW). In our research this period we pursued a reduction of the redundancy even when some source parameters annihilate. We first discovered that a Laplace estimator reduces the first order redundancy in such a source, and published the result in a Transaction of IEICE. Second, we proposed and reported a new finite window based coder, with a zero-redundancy property, which had lacked in the Laplace estimator. The CTW method can be looked as being intended to achieve the min-max redundancy. Hence we studied this target through one of a collaborated work. Especially, in this view, a most useful min-max code would be non-sequential, and in such a code, an important tool would be an optimal representation of estimated parameter in term of information metric. This is realized by a multi-dimensional two stage quantization : a combination of a first stage quantizer and a compressor for a lattice quantizer. We achieved substantial progress in this theme, which would contribute also in the study of FWCTW.