Image Coding and Data Compression

Image Coding and Data Compression

Image processing has gone through many years of research and development. But none of these techniques can be applied to compressed images, i.e., the compressed codes. Modern telecommunication and multimedia networks may require image processing and data compression being integrated into one entity. A visual information system is one example in which it is desired that image databases are constructed by storing compressed codes rather than original image pixels in order to save the storage space. Hence new algorithms should be developed for image indexing directly operated on those compressed codes. This paper reports some pioneering work in this direction to show that compressed image processing could well become a new research area and challenge right through the next century.

Image data compression is one of the most important techniques in digital image processing that used to reduce the size of image data with taking into account getting good quality of the compressed image, before several decades the researchers produced a lot of coding methods to decrease the image size, these methods include loss data compression and lossless data compression. in this research vector quantization techniques have been used to compress color image, firstly vector quantization has been applied using only cosine transform and then using principle component transform together with cosine transform, secondly vector quantization (VQ) has been achieved using wavelet transform only and then using the same transform with the principle component transform, many statistical properties have been shown and many fidelity criteria have been computed. This manuscript also includes the theory concepts of Vector Quantization, cosine transformation, principle component analysis and wavelet transform.

Polynomial IIR digital filters play a crucial role in the process of image data compression. The main purpose of designing polynomial IIR digital filters of the integer parameters space and introduce efficient filters to compress image data using a singular value decomposition algorithm. The proposed work is designed to break down the complex topic into bite-sized pieces of image data compression through the lens of compression image data using Infinite Impulse Response Filters. The frequency response of the filters is measured using a real signal with an automated panoramic measuring system developed in the virtual instrument environment. The analysis of the output signal showed that there are no limit cycles with a maximum radius of poles of 0.96 in the polynomial bandpass filters. Thus, all the functional requirements for the Integer Parameters Space of the proposed polynomial IIR digital filters were met. The results showed that the data compression and size reducing of an image file is processed without significantly impacting of visual quality. This is achieved by removing redundant or unnecessary information from the image while preserving the important details which removes unnecessary data to make the file smaller and more manageable.

Due to the large amount of data to be manipulated in image processing, efficient coding methods for data compression are necessary both for the execution of algorithms, and for transmission and storage of pictorial data. While for sequential computers many coding methods have been developed, they have been less investigated so far for parallel computers. In the present work parallel coding algorithms, mainly based upon heuristics, are developed. Great importance is attributed to data compression of binary images for filing or transmission on the one hand, and to description of the underlying stochastic scenes (from material science, biology ...), in terms of a classification by size and shape of the components of the image on the other hand.

The technique of image coding (image `compression'), resided in the research laboratory for several decades before the massive upsurge of interest in its application over the past ten years or so which now seems destined to leave the lives of few individuals in the developed world untouched. The development of the processing algorithms is discussed, together with the international determination to produce a set of standards. On the one hand, then, there were research workers around the world coding still images and video sequences at lower and lower rates, and on the other (once the discipline had matured sufficiently) increasing pressure to use the results gained in the laboratories as a practical way of overcoming the basic problem of the huge bandwidth or capacity needed to transmit or store an image of reasonable resolution in its uncoded form. The paper examines both of these activities, and their relationship, before seeing whether, indeed, there is now a logical way forward. The basic traditional techniques-prediction, transformation, motion compensation and variable wordlength coding form the basis of all activities presently proposed with the possible exception of MPEG-IV. (8 pages)

A comparison is made of the image data compression and generalization capabilities of both the backpropagation and recirculation networks. The convergence speed of the network is also examined. Simulation results show that the recirculation network has a better performance compared to the backpropagation network when used for image data compression application. The internal representation of the neural network by the concept of basis images of the weight matrix, which is helpful toward a better understanding of the principle of data compression and generalization capabilities of the neural networks, is also discussed. >

With the continuing growth of modern communications technology, demand for image transmission and storage is increasing rapidly. Advances in computer technology for mass storage and digital processing have paved the way for implementing advanced data compression techniques to improve the efficiency of transmission and storage of images. In this paper a large variety of algorithms for image data compression are considered. Starting with simple techniques of sampling and pulse code modulation (PCM), state of the art algorithms for two-dimensional data transmission are reviewed. Topics covered include differential PCM (DPCM) and predictive coding, transform coding, hybrid coding, interframe coding, adaptive techniques, and applications. Effects of channel errors and other miscellaneous related topics are also considered. While most of the examples and image models have been specialized for visual images, the techniques discussed here could be easily adapted more generally for multidimensional data compression. Our emphasis here is on fundamentals of the various techniques. A comprehensive bibliography with comments is included for a reader interested in further details of the theoretical and experimental results discussed here.

Focused on the image data storage, compression and transmission of mars rover, system design and key hardware/software architecture design are carried out in this paper based on the analysis of function requirements and key design features. Image compression algorithm and transmission protocol are proposed, and the image storage and compression module in Mars rover is designed and implemented. The multi-camera partition parallel image file system based on FPGA（Field Programmable Gate Array）is realized. The adaptive first 1 run length coding algorithm for image compression based on DSP is realized on this module. Finally the function of multi-camera image data storage and management, flexible control of image compression ratio, quality progressive transmission, coding of region of interest, anti-error code diffusion and image thumbnail downward transmission are realized in the engineering development, meeting the reliable, efficient and flexible application requirements of China’s first Mars exploration mission.

Several problems in image understanding can be solved by applying the mathematical concept of moments. In this presentation a review of the moment concept will be given, followed by a discussion of the applications of moments to image pattern recognition, change detection, quality evaluation and data compression.

Schumacher and Westmoreland [Phys. Rev. A 64, 42304 (2001)] have established a quantum analog of a well-known classical information theory result on a role of relative entropy as a measure of nonoptimality in (classical) data compression. In this paper, we provide an alternative simple and constructive proof of this result by constructing quantum compression codes (schemes) from classical data compression codes. Moreover, as the quantum data compression or coding task can be effectively reduced to a (quasi)classical one, we show that relevant results from classical information theory and data compression become applicable and therefore can be extended to the quantum domain.

Image steganography is a technique for obscuring secret message within image pixel matrix. It provides means for secretly communicating between two entities with the third party totally unaware of the embedded message. One of the most important methods for image steganography is edge detection technique, wherein secret data is inserted in LSBs of image edge pixels. Data embedding in edge pixels does not create notable distortion in image when compared to embedding data in non-edge pixels. Different types of edge pixel detectors are available including most renowned Canny edge detector, Sobel edge detector, Prewitt edge detector and many others. Hybrid edge detection uses a combination of different edge detectors for generating edge pixels of image. Message to be secretly communicated is inserted in the LSB of these edge pixels, and the image thus formed called stego image is sent to the concerned party. Major drawback of hybrid edge detection technique is its capacity. The number of edge pixels is very less in any image. In the last decade, many papers have focused on increasing capacity of edge detection by using different techniques. In this work, we have proposed an extremely high-capacity hybrid edge detection technique by using data compression. Also, data is encrypted and then inserted in the cover image pixels. For message integrity, SHA512 hash function is used. Subsequently, this work not only improves capacity of edge detection while maintaining the image quality but also provides security and message integrity.

Progressive image coding by hierarchical linear approximation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.

Independent component analysis of high-resolution imaging data identifies distinct functional domains