Thresholding and quantization algorithms for image compression techniques: a review

With increasing demand on digital images, there is a need to compress the image to entertain the limited bandwidth and storage capacity. Recently, there is a growing interest among researchers focusing on compression of various types of images and data. Amongst various compression algorithms, transform-based compression is one of the promising algorithms. Despite the technological advances in transmission and storage, the demands placed on the bandwidth of communication and storage capacities by far outstrips its availability. This paper presents a review of image compression principle, compression techniques and various thresholding algorithms (pre-processing algorithms) and quantization algorithm (post-processing algorithms). This paper intends to give an overview to the relevant parties to choose the suitable image compression algorithms to suit with the need

Improved thresholding and quantization techniques for image compression

In recent decades, digital images have become increasingly important. With many modern applications use image graphics extensively, it tends to burden both the storage and transmission process. Despite the technological advances in storage and transmission, the demands placed on storage and bandwidth capacities still exceeded its availability. Moreover, the compression process involves eliminating some data that degrades the image quality. Therefore, to overcome this problem, an improved thresholding and quantization techniques for image compression is proposed. Firstly, the generated wavelet coefficients obtained from the Discrete Wavelet Transform (DWT) process are thresholded by the proposed Standard Deviation-Based Wavelet Coefficients Threshold Estimation Algorithm. The proposed algorithm estimates the best threshold value at each detail subbands. This algorithm exploits the huge number of near-zero coefficients exist in detail subbands. For different images, the distribution of wavelet coefficients at each subband are substantially different. So, by calculating the standard deviation value of each subband, a better threshold value can be obtained. Next, the retained wavelet coefficients are subjected to the next proposed Minimizing Median Quantization Error Algorithm. The proposed algorithm utilizes the high occurrence of zero coefficient obtained by the previous thresholding process by re-allocating the zero and non-zero coefficients in different groups for quantization. Then, quantization error minimization mechanism is employed by calculating the median quantization error at each quantization interval class. The results are then compared to the existing algorithms and it is found that the proposed compression algorithm shows double increase in compression ratio performance, produces higher image quality with PSNR value above 40dB and ensures a better bit saving with smooth control at bit rate higher than 4 bpp. Thus, the proposed algorithm provides an alternative technique to compress the digital image

Wavelet-based image compression for mobile applications.

The transmission of digital colour images is rapidly becoming popular on mobile telephones, Personal Digital Assistant (PDA) technology and other wireless based image services. However, transmitting digital colour images via mobile devices is badly affected by low air bandwidth. Advances in communications Channels (example 3G communication network) go some way to addressing this problem but the rapid increase in traffic and demand for ever better quality images, means that effective data compression techniques are essential for transmitting and storing digital images. The main objective of this thesis is to offer a novel image compression technique that can help to overcome the bandwidth problem. This thesis has investigated and implemented three different wavelet-based compression schemes with a focus on a suitable compression method for mobile applications. The first described algorithm is a dual wavelet compression algorithm, which is a modified conventional wavelet compression method. The algorithm uses different wavelet filters to decompose the luminance and chrominance components separately. In addition, different levels of decomposition can also be applied to each component separately. The second algorithm is segmented wavelet-based, which segments an image into its smooth and nonsmooth parts. Different wavelet filters are then applied to the segmented parts of the image. Finally, the third algorithm is the hybrid wavelet-based compression System (HWCS), where the subject of interest is cropped and is then compressed using a wavelet-based method. The details of the background are reduced by averaging it and sending the background separately from the compressed subject of interest. The final image is reconstructed by replacing the averaged background image pixels with the compressed cropped image. For each algorithm the experimental results presented in this thesis clearly demonstrated that encoder output can be effectively reduced while maintaining an acceptable image visual quality particularly when compared to a conventional wavelet-based compression scheme

Super-Resolution Enhancement of Digital Video

SR from digital video is a relatively new field, in only its third decade of existence. There is no doubt that as imaging sensor technologies, optical fabrication techniques, and computational algorithms mature, SR will find its way into digital video products such as cameras and digital cable set-top boxes. These papers on the fundamental SR topics of image registration, regularization, photometric diversity, detector nonuniformity, compression, optical design, and performance metrics serve as pioneers in the dynamic and evolving field of SR image reconstruction research and development. We are proud to present them to the image and video processing research community. (Refers to papers appearing in the same issue of the EURASIP Journal on Advances in Signal Processing in which this editorial appeared.

Investigation of the effects of image compression on the geometric quality of digital protogrammetric imagery

We are living in a decade, where the use of digital images is becoming increasingly important. Photographs are now converted into digital form, and direct acquisition of digital images is becoming increasing important as sensors and associated electronics. Unlike images in analogue form, digital representation of images allows visual information to· be easily manipulated in useful ways. One practical problem of the digital image representation is that, it requires a very large number of bits and hence one encounters a fairly large volume of data in a digital production environment if they are stored uncompressed on the disk. With the rapid advances in sensor technology and digital electronics, the number of bits grow larger in softcopy photogrammetry, remote sensing and multimedia GIS. As a result, it is desirable to find efficient representation for digital images in order to reduce the memory required for storage, improve the data access rate from storage devices, and reduce the time required for transfer across communication channels. The component of digital image processing that deals with this problem is called image compression. Image compression is a necessity for the utilisation of large digital images in softcopy photogrammetry, remote sensing, and multimedia GIS. Numerous image Compression standards exist today with the common goal of reducing the number of bits needed to store images, and to facilitate the interchange of compressed image data between various devices and applications. JPEG image compression standard is one alternative for carrying out the image compression task. This standard was formed under the auspices ISO and CCITT for the purpose of developing an international standard for the compression and decompression of continuous-tone, still-frame, monochrome and colour images. The JPEG standard algorithm &Us into three general categories: the baseline sequential process that provides a simple and efficient algorithm for most image coding applications, the extended DCT-based process that allows the baseline system to satisfy a broader range of applications, and an independent lossless process for application demanding that type of compression. This thesis experimentally investigates the geometric degradations resulting from lossy JPEG compression on photogrammetric imagery at various levels of quality factors. The effects and the suitability of JPEG lossy image compression on industrial photogrammetric imagery are investigated. Examples are drawn from the extraction of targets in close-range photogrammetric imagery. In the experiments, the JPEG was used to compress and decompress a set of test images. The algorithm has been tested on digital images containing various levels of entropy (a measure of information content of an image) with different image capture capabilities. Residual data was obtained by taking the pixel-by-pixel difference between the original data and the reconstructed data. The image quality measure, root mean square (rms) error of the residual was used as a quality measure to judge the quality of images produced by JPEG(DCT-based) image compression technique. Two techniques, TIFF (IZW) compression and JPEG(DCT-based) compression are compared with respect to compression ratios achieved. JPEG(DCT-based) yields better compression ratios, and it seems to be a good choice for image compression. Further in the investigation, it is found out that, for grey-scale images, the best compression ratios were obtained when the quality factors between 60 and 90 were used (i.e., at a compression ratio of 1:10 to 1:20). At these quality factors the reconstructed data has virtually no degradation in the visual and geometric quality for the application at hand. Recently, many fast and efficient image file formats have also been developed to store, organise and display images in an efficient way. Almost every image file format incorporates some kind of compression method to manage data within common place networks and storage devices. The current major file formats used in softcopy photogrammetry, remote sensing and · multimedia GIS. were also investigated. It was also found out that the choice of a particular image file format for a given application generally involves several interdependent considerations including quality; flexibility; computation; storage, or transmission. The suitability of a file format for a given purpose is · best determined by knowing its original purpose. Some of these are widely used (e.g., TIFF, JPEG) and serve as exchange formats. Others are adapted to the needs of particular applications or particular operating systems

Image Compression Techniques: A Survey in Lossless and Lossy algorithms

The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression

Roadmap on optical security

Postprint (author's final draft

Wavelet-based image compression for mobile applications

The transmission of digital colour images is rapidly becoming popular on mobile telephones, Personal Digital Assistant (PDA) technology and other wireless based image services. However, transmitting digital colour images via mobile devices is badly affected by low air bandwidth. Advances in communications Channels (example 3G communication network) go some way to addressing this problem but the rapid increase in traffic and demand for ever better quality images, means that effective data compression techniques are essential for transmitting and storing digital images. The main objective of this thesis is to offer a novel image compression technique that can help to overcome the bandwidth problem. This thesis has investigated and implemented three different wavelet-based compression schemes with a focus on a suitable compression method for mobile applications. The first described algorithm is a dual wavelet compression algorithm, which is a modified conventional wavelet compression method. The algorithm uses different wavelet filters to decompose the luminance and chrominance components separately. In addition, different levels of decomposition can also be applied to each component separately. The second algorithm is segmented wavelet-based, which segments an image into its smooth and nonsmooth parts. Different wavelet filters are then applied to the segmented parts of the image. Finally, the third algorithm is the hybrid wavelet-based compression System (HWCS), where the subject of interest is cropped and is then compressed using a wavelet-based method. The details of the background are reduced by averaging it and sending the background separately from the compressed subject of interest. The final image is reconstructed by replacing the averaged background image pixels with the compressed cropped image. For each algorithm the experimental results presented in this thesis clearly demonstrated that encoder output can be effectively reduced while maintaining an acceptable image visual quality particularly when compared to a conventional wavelet-based compression scheme.EThOS - Electronic Theses Online ServiceGBUnited Kingdo