Development of multidimensional variable rate sampling method and its application to high definition image cmmunications

利用可变速率采样的系统的成功设计的关键是如何调整由于采样和量化的失真之间的折衷。这是很难设计的采样器和量化器，同时最佳的输入信号，因为量化器是内置在递归闭环的编码器使用自适应采样。在本计画中，我们利用在编码器闭合回路中以广义压扩模型为基础的向量量化器来解决这个问题。现将本课题的研究结果总结如下。(1)在采样率和量化器比特率的许多不同条件下使用再现图像进行客观评估测试。实验结果表明，客观再现质量与主观质量测度信噪比（SNR）之间具有较好的一致性。(2)研究了信噪比与采样率、量化器比特率的关系以及这些参数之间的相互关系。结果表明，只要简单地使采样和量化引起的总失真最小化，就能获得良好的编码性能。(3)为了控制由于采样和量化引起的失真，开发了两种图像数据压缩方法。一种是基于分段双线性片的矢量量化方法，另一种是基于递归矢量量化方法。在这两个系统中，采样率自适应控制，以适应图像的局部特征。(4)提出了一种基于二次范数压扩模型的压扩矢量量化器的设计方法。(5)提出了矢量量化的快速编码算法。

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.