| Project/Area Number |
03650277
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
電子通信系統工学
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| Research Institution | Ehime University |
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Principal Investigator |
YAMADA Yoshio Ehime University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (00110833)
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| Co-Investigator(Kenkyū-buntansha) |
TAZAKI Saburo Ehime University, Faculty of Engineering, Professor, 工学部, 教授 (00036394)
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| Project Period (FY) |
1991 – 1993
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| Project Status |
Completed (Fiscal Year 1993)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1993: ¥200,000 (Direct Cost: ¥200,000)
Fiscal Year 1992: ¥300,000 (Direct Cost: ¥300,000)
Fiscal Year 1991: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | variable rate sampling / vector quantization / high definition image / データ圧縮 / 高精細画像信号 / 画質評価尺度 / デ-タ圧縮 |
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| Research Abstract |
The key to the successful design of the systems that utilize the variable rate sampling is the way of ajusting trade-offs between the distortions due to sampling and quantization. It is difficult to design the sampler and quantizer that are simultaneously optimum to the input signal since the quantizer is built in the recursive closed loop of the encoder using adaptive sampling. In this project, we utilize the vector quantizer based on the generalized companding model within the encoder closed loop for resolving this problem. The results of this project are summaried as follows.
(1) Objective evaluation tests are conducted using reproduced images under many different conditions on sampling rate and quantizer bit rate. It is found that good coincidence is observed between the objective reproduction quality of image and the subjective quality measure, SNR.
(2) Moreover, the investigations are done on the SNR vs.sampling rate and the SNR vs.the quantizer bit rate, and the mutual relations of these parameters. It is found that simply minimizing the total distotions due to sampling and quantization gives good coding performance.
(3) In order to control the distortions due to sampling and quantization, two image data compression methods are developed. One of them is based on the vector quantization of the piece-wise bi-linear patch, and the other is based on the recursive vector quantization mothod. In both systems, the sampling rate is adaptively controlled so as to fit the local feature of the image.
(4) A method for designing companded vector quantizers that is described by using the quadratic-norm based companding model is developed.
(5) Fast encoding algorithms of vector quantization are developed.
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