Abstract not found

Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. Centralized data collection and analysis adversely impact sensor node lifetime. Previous sensor network research has, therefore, focused on in network aggregation and query processing, but has done so for applications where the features of interest are known a priori. When features are not known a priori, as is the case with many scientific applications in dense sensor arrays, efficient support for multi-resolution storage and iterative, drill-down queries is essential.

We present a full- and no-reference blur metric as well as a full-reference ringing metric. These metrics are based on an analysis of the edges and adjacent regions in an image and have very low computational complexity. As blur and ringing are typical artifacts of wavelet compression, the metrics are then applied to JPEG2000 coded images. Their perceptual significance is corroborated through a number of subjective experiments. The results show that the proposed metrics perform well over a wide range of image content and distortion levels. Potential applications include source coding optimization and network resource management. r 2003 Elsevier B.V. All rights reserved.

JPEG2000 is an emerging standard for still image compression and is becoming the solution of choice for many digital imaging fields and applications. An important aspect of JPEG2000 is its “compress once, decompress many ways” property [1], i. e., it allows extraction of various sub-images (e.g., images with various resolutions, pixel fidelities, tiles and components) all from a single compressed image codestream. In this paper, we present a flexible and scalable authentication scheme for JPEG2000 images based on the Merkle hash tree and digital signature. Our scheme is fully compatible with JPEG2000 and possesses a “sign once, verify many ways ” property. That is, it allows users to verify the authenticity and integrity of different sub-images extracted from a single compressed codestream protected with a single digital signature. Categories and Subject Descriptors K.4.4 [Computers and Society]: Electronic Commerce— intellectual property, security; I.3.8 [Computer Methodologies]:

Nowadays, video surveillance of people must ensure privacy. In this paper, we propose a seamless solution to that problem by masking faces in video sequences, which keeps people anonymous. The system consists of two modules. First, an analysis module identifies and follows regions of interest (ROI's) where faces are detected. Second, the JPEG 2000 encoding module compresses the frames keeping the ROI’s in a separate data layer, so that the correct rendering of human faces can be restricted. The analysis module combines two complementary methods: face detection, to locate faces in the image, and tracking, to follow them seamlessly along the time. The fusion of these two methods increases robustness: once a face has already been detected in a frame, tracking may locate it in the consecutive frames, even when a face detection algorithm would not. In addition, detection of faces prevents tracking from loosing its targets. The encoding module downshifts the JPEG 2000 data corresponding to the identified ROI's to the lowest quality layer of the codestream. When the transmission bandwidth is limited, the human faces will then be decoded with a lower visual quality, up to invisibility when required. The proposed solution has been tested on different types of sequences. The results are presented in the paper. 1.

This paper describes an objective comparison of the image quality of different encoders. Our approach is based on estimating the visual impact of compression artifacts on perceived quality. We present a tool that measures these artifacts in an image and uses them to compute a prediction of the Mean Opinion Score (MOS) obtained in subjective experiments. We show that the MOS predictions by our proposed tool are a better indicator of perceived image quality than PSNR, especially for highly compressed images. For the encoder comparison, we compress a set of 29 test images with two JPEG encoders (Adobe Photoshop and IrfanView) and three JPEG2000 encoders (JasPer, Kakadu, and IrfanView) at various compression ratios. We compute blockiness, blur, and MOS predictions as well as PSNR of the compressed images. Our results show that the IrfanView JPEG encoder produces consistently better images than the Adobe Photoshop JPEG encoder at the same data rate. The differences between the JPEG2000 encoders in our test are less pronounced; JasPer comes out as the best codec, closely followed by IrfanView and Kakadu. Comparing the JPEG- and JPEG2000-encoding quality of IrfanView, we find that JPEG has a slight edge at low compression ratios, while JPEG2000 is the clear winner at medium and high compression ratios.

In article, propose a novel integrated approach to quantitative semi-fragile authentication JPEG2000 images under a generic framework which combines infrastructures Firstly acceptable mani- pulations (e.g., re-encoding) which should authen- tication defined based considerations some target applications. propose unique concept of lowest authenticable rate images undergoing repetitive encoding guarantee pass authentication provided re-encoding rates above lowest allowable solutions include computation of content-based features derived from EBCOT encoding procedure of JPEG2000, error correction coding derived features, cryptographic signing, finally robust embedding of feature codes image watermarks. Keywords digital signature, PKI, watermarking, image authentication, JPEG2000, correction coding

Object recognition and extraction have been of considerable research interest in digital photogrammetry for many years. As a result, many conventional tasks have been successfully automated but, despite some advances, the automatic extraction of buildings remains an open research question. Machine learning techniques have received little attention from the photogrammetric community in their search for methods of object extraction. While these techniques cannot provide all the answers, they do offer some potential benefits in the early stages of visual processing. This paper presents the results of an investigation into the use of machine learning in the form of a support vector machine. The images are characterized using wavelet analysis to provide multi-resolution data for the machine learning phase. 1.

Abstract—In this paper, a novel multiple description coding technique is proposed, based on optimal Lagrangian rate allocation. The method assumes the coded data consists of independently coded blocks. Initially, all the blocks are coded at two different rates. Then blocks are split into two subsets with similar rate distortion characteristics; two balanced descriptions are generated by combining code blocks belonging to the two subsets encoded at opposite rates. A theoretical analysis of the approach is carried out, and the optimal rate distortion conditions are worked out. The method is successfully applied to the JPEG 2000 standard and simulation results show a noticeable performance improvement with respect to state-of-the art algorithms. The proposed technique enables easy tuning of the required coding redundancy. Moreover, the generated streams are fully compatible with Part 1 of the standard. Index Terms—Error resilience, JPEG 2000, multiple description coding, rate-distortion optimization.

Abstract — In this paper, we propose a new object-based video coding/transmission system using the emerging Motion JPEG 2000 standard [1] for the efficient storage and delivery of video surveillance over low bandwidth channels. Some recent papers deal with JPEG 2000 coding/transmission based on the Region Of Interest (ROI) feature and the multi-layer capability provided by this coding system [2][3]. Those approaches allow delivering more quality for mobile objects (or ROI) than for the background when bandwidth is too narrow for a sufficient video quality. The method proposed here provides the same features while significantly improving the average bitrate/quality ratio of delivered video when cameras are static. We transmit only ROIs of each frame as well as an automatic estimation of the background at a lower frame rate in two separate Motion JPEG 2000 streams. The frames are then reconstructed at the client side without the need of other external data. Our method provides both better video quality and reduced client CPU usage with negligible storage overhead. Video surveillance streams stored on the server are fully compliant with existing Motion JPEG 2000 decoders.