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.

Recent development in multimedia processing and network technologies has facilitated the distribution and sharing of multimedia through networks, and increased the security demands of multimedia contents. Traditional image content protection schemes use extrinsic approaches, such as watermarking or fingerprinting. However, under many circumstances, extrinsic content protection is not possible. Therefore, there is great interest in developing forensic tools via intrinsic fingerprints to solve these problems. Source coding is a common step of natural image acquisition, so in this paper, we focus on the fundamental research on digital image source coder forensics via intrinsic fingerprints. First, we investigate the unique intrinsic fingerprint of many popular image source encoders, including transform-based coding (both discrete cosine transform and discrete wavelet transform based), subband coding, differential image coding, and also block processing as the traces of evidence. Based on the intrinsic fingerprint of image source encoders, we construct an image source coding forensic detector that identifies which source encoder is applied, what the coding parameters are along with confidence measures of the result. Our simulation results show that the proposed system provides trustworthy performance: for most test cases, the probability of detecting the correct source encoder is over 90%.

In this letter, we show that the performance of a wavelet decoder can be improved via a set theoretic approach at low bit rates. The traditional decoding rule is replaced by iterative projection-onto-convex-set (POCS) operations to exploit a priori information about the quantization and geometric constraints of the image source. Experimental results have shown that the proposed set theoretic estimation based scheme is capable of improving both the subjective and the objective performance of wavelet decoders.

This paper presents a newly developed adaptive fuzzy post-filter for removing blocking and ringing artifacts in the highly compressed images and videos. The proposed post-filter adaptively adjusts its filtering window size and the spread parameter in the fuzzy membership function according to the variance value in an edge map. The post-filtering operation is directly applied to the classified edge blocks in the image under the guidance of the edge map, while keeping smooth and textured blocks unchanged. The experimental results demonstrate that the proposed method possesses a good edge preserving property and can maximally remove coding artifacts. Compared with our previous method, the proposed post-filter achieves better objective and subjective quality and further reduces computational complexity.

In this article, we propose a novel method to reduce ringing artifacts in BDCT-encoded cartoon images using image analogies. The quantization procedure of BDCT compression (such as JPEG and MPEG) introduces annoying visual artifacts. Our main focus is on the removal of ringing artifacts that is seldom addressed by existing methods. In the proposed method, the contaminated image is modeled as a Markov random field (MRF). We “learn ” the behavior of contamination by extracting massive numbers of artifact patterns from a training set, and organizing them using tree-structured vector quantization (TSVQ). Instead of postfiltering the input contaminated image, we synthesize an artifact-reduced image. Our method is noniterative and hence, can remove artifacts within a very short period of time. We show that substantial improvement is achieved using the proposed method in terms of visual quality and statistics.

Abstract — A new perceptually adaptive joint deringing-deblocking filtering method for wavelet based scalable video coding is proposed. A structure based on in-loop filtering is designed for the decoding of scalable coded video considering the update step in motion compensated temporal filtering. Since ringing and blocking artifacts are visually disturbing, the characteristics of the human visual system (HVS) are considered in the underlying Bilateral filtering. In the proposed approach, the filter strength is adjusted according to the perceptual distortion by integrating the HVS models of luminance masking, activity masking and temporal masking. Consequently, different filtering strength is applied to areas with different perceptual sensitivity in terms of the HVS. A comparative evaluation using conventional loop filtering structure and Bilateral filter was conducted. The experimental assessment demonstrates a superior performance of the proposed adaptive filtering, providing substantial and consistent better objective and subjective quality. Keywords- scalable video coding; deringing; deblocking; bilateral filter; human visual system I.

Measurement of image quality is crucial for many imageprocessing algorithms, such as acquisition, compression, restoration, enhancement and reproduction. Traditionally, image quality assessment algorithms have focused on measuring image fidelity, where quality is measured as fidelity with respect to a `reference' or `perfect' image. The field of blind quality assessment has been largely unexplored. In this paper we present an algorithm for blindly determining the quality of JPEG2000 compressed images. Our algorithm assigns quality scores that are in good agreement with data from human observers. Our algorithm utilizes a statistical model for wavelet coefficients and computes features that exploit the fact that quantization produces more zero coefficients than expected for natural images. The algorithm is trained and tested on data obtained from human observers, and performs close to the limit on useful prediction imposed by the variability between human subjects.

In this digital era, digital multimedia contents are often transmitted over networks without any protection. This raises serious security concerns since the receivers/subscribers do not know what processes have been applied to multimedia data, and neither do they know whether this copy comes from a trusted source. Therefore, it is critical to provide forensic tools to identify the history of operations applied to multimedia data. In this paper, we focus on the identification of source coding techniques applied to multimedia, and we investigate the forensic analysis of transform based coding (both DCT and DWT based), subband coding, and linear predictive coding. Using the intrinsic fingerprints as trace of evidences, we construct an image source coding forensic system that analyzes which source encoder is used to compress the image and provides confidence measurements. Our simulation results show that the proposed system provides trustworthy performance: the probability of detecting the correct source encoder is 0.82 when PSNR = 40 dB, and it can correctly identify the source encoder with probability 0.98 with PSNR = 20 dB. Index Terms — Multimedia forensics, security, image coding

Abstract not found