Compressed image processing - a new research area and challenge

The first quarter of 2007 started as an active season for the real-time image processing community. The annual SPIE Conference on Real-Time Image Processing, which was held in San Jose, CA, highlighted recent developments in this growing branch of image processing. As one of the few regular events with its focus matching that of JRTIP, one witnessed the growth in applications of real-time image and video processing. The conference served as an effective instrument to foster the dissemination of recent results and to leverage the cohesion of the real time image processing community. The preparations for the next SPIE Conference on Real-Time Image Processing in 2008 are already progressing and its call-for-papers is included at the end of this issue. In 2007, JRTIP is going to seek involvement of more scientists in the field of real-time image and video processing. After 1 year of operation, the editors-in-chief have started to recruit additional associate editors to enlarge the expertise base of the editorial board, to solicit manuscript submissions from potential authors, and to make the reviewing process more efficient. Interested colleagues should contact the editors-in-chief expressing their Associate Editor candidature in support of one or more fields within the focus of JRTIP. In addition, they wish to encourage researchers to act as guest editors for special issues on any real-time image or video processing related topics of interest to them. It is possible to schedule two special issues in 2007, provided that a draft call-for-papers is sent to the editors-in-chief as soon as possible. The third issue of JRTIP presents four articles, two are survey papers and two are original research papers. These articles address different real-time image processing perspectives ranging from algorithmic to hardware issues. The first survey paper deals with the real-time filtering in 2D and 3D using ordered statistics that are supported by a rich set of experimental results derived from several algorithmic variations. Performance measurements are used to demonstrate the effect of noise suppression and preservation of fine image structures and to show the DSP implementation of denoising filters. The second survey paper introduces a non-invasive analysis of dynamic processes in living cells using microscopic computer vision techniques for understanding the interactions between cellular and molecular functional units and for providing novel insights into the cell biology. The third article is an original research paper covering a machine vision application involving vision-based position sensing for aerial refueling of unmanned aerial vehicles in real-time. A full image processing chain is investigated and tested in a MATLAB/Simulink soft real-time environment as well as in a Linux/RTAI hard real-time environment. The feasibility of the real-time implementation is shown using off-the-shelf commercial hardware. The fourth article is another original research paper describing a hardware architecture for real-time fractal coding for image compression. The introduced parallel hardware architecture contains units for domain M. F. Carlsohn (&) Ingenieurberatung fur Computer Vision und Bildkommunikation, Am Heiddamm 36g, 28355 Bremen, Germany e-mail: matthias.carlsohn@t-online.de

Image processing is a method for enhancing unprocessed images from cameras on aircraft, spacecraft, and satellites as well as images taken regularly for a variety of uses. In general, the following strategies can be used to categorize all image processing operations: Images are represented in several ways, which are referred to as image representation, image preprocessing, image enhancement, image restoration, image analysis, picture reconstruction, and image data compression. The first radiometric normalization, geometric distortion correction, and noise removal of the raw image data been discussed in the past. The goal of the information extraction procedures is to automate the identification of tone in a scene by replacing the visual examination of image data with quantitative techniques. This entails analyzing multispectral image data and establishing the earth cover identification of each pixel in an image using statistically based decision procedures. The goal of the classification procedure is to sort all of the pixels in a digital image into one of several different earth cover classes or themes. The purpose of this study is to examine various image processing approaches and algorithms, many sorts of image processing algorithms: Optical Character Recognition (OCR) and Supporting Vector Machine (SVM) a feature extraction technique, on the vehicle classification dataset and had accurate results of 90% for SVM and 95% for OCR, to further improve the performance of machine algorithms in terms of accuracy for image processing technique using a vehicle. This study can be used for vehicle classification research, it also advances and improves the performance of the system in terms of accurate detection.

Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement

Image Coding and Data Compression

Image Coding and Data Compression

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.

Ground Penetrating Radar (GPR), a high frequency electromagnetic prospecting method (10 to 3000 MHz) has been rapidly developed in recent decades. With many advantages such as non-destructive, fast data collection, high precision and resolution, this method is a useful means to detect underground targets. It is currently used in the research of studying the shallow structure for examples: forecast landslide, subsidence, mapping urban underground works, traffic, construction, archaeology and other various fields of engineering, GPR data processing is becoming increasingly urgent. Wavelet transform is one of the new signal analysis tools, plays a vital role in numerous domains like image processing, graphics, data compression, gravitational, electromagnetic and geomagnetic data processing, GPR and some others. In this study, we used the continuous wavelet transform (CWT) and multiscale edge detection (MED) with the wavelet functions which were appropriately selected to determine underground targets. The accuracy of this technique was tested on some theoretical models before being applied on experimental data. The obtained results showed that this was a feasible method that could be used to detect the size and position of the anomaly objects.

Preparing a Two Column Paper with MS Word for Windows

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.

1 - Multimedia Networking and Communication: Principles and Challenges

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.

We present a content-based query system in an image database. Queries are based on color, shape content and textual description of images. We show that without efficient synergy between image analysis techniques, more particularly mathematics, and database technology, it is very difficult to find effective, feasible and efficient solutions to the problem of retrieval in visual information systems. We focus our presentation on the usefulness of mathematics representation of color and shape, and its integration in the database. Finally, we present the usefulness of knowledge discovery in the extraction of shared features of relevant feedback.

Quantum computers are known to efficiently solve problems that remain unsolved by classical von Neumann-type computers. However, in the research and development phase within the field of information science, quantum information theory is prioritized, and the extent of information processing achievable at the experimental stage is generally not well understood. In particular, data compression is a key concept and challenge in image processing and multimedia processing within quantum information processing. A prime example of data compression in quantum computing is the quantum Fourier transform. With the advent of experimental environments enabling simplified simulations using quantum simulators developed by Google and IBM, we considered that verification might now be possible. Still images captured by 360 degree cameras are full-sky images, requiring pixel counts equivalent to 4K to 8K. This results in significantly larger data volumes compared to standard high-definition still images. Therefore, when encoding these images, it is desirable to efficiently reduce the information volume while preserving quality as much as possible. We considered whether insights from quantum information computing science could be applied to achieve this. Therefore, in this study, we experimentally investigated the effectiveness, usefulness, and potential of quantum information science for image compression by simulating the quantum Fourier transform using Qiskit during the data compression process of 360 degree camera images.

This article reviews the various image processing techniques in MATLAB and also hardware implementation in FPGA using Xilinx system generator. Image processing can be termed as processing of images using mathematical operations by using various forms of signal processing techniques. The main aim of image processing is to extract important features from an image data and process it in a desired manner and to visually enhance or to statistically evaluate the desired aspect of the image. This article provides an insight into the various approaches of Digital Image processing techniques in Matlab. This article also provides an introduction to FPGA and also a step by step tutorial in handling Xilinx System Generator. The Xilinx System Generator tool is a new application in image processing and offers a friendly environment design for the processing. This tool support software simulation, but the most important is that can synthesize in FPGAs hardware, with the parallelism, robust and speed, this features are essentials in image processing. Implementation of these algorithms on a FPGA is having advantage of using large memory and embedded multipliers. Advances in FPGA technology with the development of sophisticated and efficient tools for modelling, simulation and synthesis have made FPGA a highly useful platform.