Construction of multi-level fluorescent switch using photochromic diarylethene and fluorescent dye

In this paper, the authors propose a new method for designing predictors suitable for lossless image coding. In recent years, lossless coding systems based on optimal design of predictors for each image have been studied. In these systems, the linear prediction coefficients are determined so as to minimize the mean squared prediction errors. In lossless image coding, however, where the ultimate goal is to reduce the coding rate, minimizing the mean squared prediction errors does not necessarily yield the best results. Therefore, in order to reduce the coding rate directly, the authors attempted to formulate the amount of information on the prediction errors and design the predictors so as to minimize that value. Moreover, the image is divided into blocks, and these blocks are classified into multiple classes; multiple predictors for adaptive prediction are optimized at the same time by repeatedly executing the design of the predictor for each class and updating the class for each block, based on the cost representing the amount of information. In the results from a coding simulation, this system demonstrated superior coding efficiency compared to the conventional method for minimizing the mean squared prediction errors, and was confirmed to achieve a coding rate of 0.37 bitspel lower than JPEG-LS which is an international standard for lossless image coding. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(5): 90– 98, 2007; Published online in Wiley InterScience (). DOI 10.1002sscj.10318

Design and Evaluation of Minimum-Rate Predictors for Lossless Image Coding

Although orthogonal transform coding exhibits superior coding efficiency at low rates, it shows a decreased picture quality of reproduced images as a result of noises unique to transform coding, such as mosquito noise and block distortion.



This paper proposes a new transform coding scheme, which has a high coding efficiency and significantly reduced noises. It is based on applications of the concepts of region-oriented coding, to orthogonal transform coding, and has been attracting attention in recent years. The proposed scheme expresses all the regions by quadrilateral blocks to reduce the number of bits required for coding a region shape. Block segmentations are realized with stability and certainty by repeatedly transforming the shapes of the blocks with the square block as the initial shape.



The approximating accuracy of an image is improved by the mean values of the blocks by using a smoothing filter. In addition, KL-transform coding derived from a correlation function model of images is applied to the luminance elements of each block, and bit allocation is optimized on the basis of this model; and the coding efficiency is improved by using an adaptive control method. Simulation results confirm that this method realizes a coding efficiency which surpasses that of the conventional orthogonal transform coding based on square blocks, and gives visually good reproduced images.

Adaptive KL‐transform coding of images based on variable block shapes

Status Report on Image Information Systems and Image Data Base Technology

A high-performance lossless compression system for satellite NOAA data is developed. The data is called 'high resolution picture transmission' (HRPT) data, and consists of around 93 percent advanced very high resolution radiometer (AVHRR) multi-channel image data and 7 percent of miscellaneous data. In compressing the image portion, we classify each pixel into 10 different groups and apply a multi-channel prediction and a non-linear error conversion. The entropy coder is an arithmetic coder which is adaptive and regenerates the approximation of the statistical properties of the source as an initial probability table. To compress the non-image part, we used the general compressor (gzip). From experimental results, the original information is compressed down to 25 percent to approx. 40 percent.

https://ntrs.nasa.gov/api/citations/19950008178/downloads/19950008178.pdf

Lossless compression of NOAA-AVHRR satellite data

To construct efficiently an image database system for high-tone images, a variable-length, code-selective reversible predictive coding scheme with a high-compression ratio is proposed. This coding scheme is based on application of the quantizer-selective method in the DPCM coding [11] (previously proposed by the present authors) to an adaptive selection of a variable-length code. This is an adaptive coding scheme in which a suitable code is selected from the pre-designed variable-length codes for each pel. This code selection method requires no additional information, since the code selection is carried out based only on information of pels for which the coding has already been completed. The predesigned variable-length codes and the threshold values for the code selection are determined by a model of the probability distribution of transformed prediction-error signals. The initial setting of coding parameters is constant and independent of the image to be encoded. This scheme has, therefore, much generality that is independent of any kinds of images. The coding rate of this scheme is improved by about 0.3 to 0.5 bit/pel compared with the optimal nonadaptive coding scheme using a single Huffman code. It was confirmed that the proposed scheme has the highest coding efficiency compared with several conventional representative reversible coding schemes.

Variable‐length‐code‐selective reversible predictive coding for multilevel images

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Variable‐length‐code‐selective reversible predictive coding for multilevel images