Zhigang Fan

Identification of bitmap compression history: JPEG detection and quantizer estimation

Sometimes image processing units inherit images in raster bitmap format only, so that processing is to be carried without knowledge of past operations that may compromise image quality (e.g., compression). To carry further processing, it is useful to not only know whether the image has been previously JPEG compressed, but to learn what quantization table was used. This is the case, for example, if one wants to remove JPEG artifacts or for JPEG re-compression. In this paper, a fast and efficient method is provided to determine whether an image has been previously JPEG compressed. After detecting a compression signature, we estimate compression parameters. Specifically, we developed a method for the maximum likelihood estimation of JPEG quantization steps. The quantizer estimation method is very robust so that only sporadically an estimated quantizer step size is off, and when so, it is by one value.

Optimizing block-thresholding segmentation for multilayer compression of compound images

Compound document images contain graphic or textual content along with pictures. They are a very common form of documents, found in magazines, brochures, Web sites, etc. We focus our attention on the mixed raster content (MRC) multilayer approach for compound image compression. We study block thresholding as a means to segment an image for MRC. An attempt is made to optimize the block threshold in a rate-distortion sense. Also, a fast algorithm is presented to approximate the optimized method. Extensive results are presented including rate-distortion curves, segmentation masks and reconstructed images, showing the performance of the proposed algorithm.

A robust technique for image descreening based on the wavelet transform

In this correspondence, a novel wavelet-based approach to recover continuous-tone (contone) images from halftone images is presented. Wavelet decomposition of the halftone image facilitates a series of spatial and frequency selective processing to preserve most of the original image contents while eliminating the halftone noise. Furthermore, optional nonlinear filtering can be applied as a postprocessing stage to create the final aesthetic contone image. This approach lends itself to practical applications since it is independent of parameter estimation and, hence, universal to all types of halftoned images, including those obtained by scanning printed halftones.

Adaptive Sparse Representations for Video Anomaly Detection

Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. A common class of approaches relies on object tracking and trajectory analysis. Very recently, sparse reconstruction techniques have been employed in video anomaly detection. The fundamental underlying assumption of these methods is that any new feature representation of a normal/anomalous event can be approximately modeled as a (sparse) linear combination prelabeled feature representations (of previously observed events) in a training dictionary. Sparsity can be a powerful prior on model coefficients but challenges remain in the detection of anomalies involving multiple objects and the ability of the linear sparsity model to effectively allow for class separation. The proposed research addresses both these issues. First, we develop a new joint sparsity model for anomaly detection that enables the detection of joint anomalies involving multiple objects. This extension is highly nontrivial since it leads to a new simultaneous sparsity problem that we solve using a greedy pursuit technique. Second, we introduce nonlinearity into, that is, kernelize. The linear sparsity model to enable superior class separability and hence anomaly detection. We extensively test on several real world video datasets involving both single and multiple object anomalies. Results show marked improvements in detection of anomalies in both supervised and unsupervised scenarios when using the proposed sparsity models.

JPEG decompression with reduced artifacts

The major artifacts of JPEG compressed images are blocking and ringing, which are mainly due to the quantization of low frequency and high frequency DCT components respectively. The ringing artifacts are particularly dominant in document images, where sharp edges present commonly in text and graphics are more likely to be encountered. In this paper we describe a decompression algorithm with reduction in both ringing and blocking artifacts.

JPEG compression history estimation for color images

We routinely encounter digital color images that were previously JPEG-compressed. We aim to retrieve the various settings - termed JPEG compression history (CH) - employed during previous JPEG operations. This information is often discarded en-route to the images current representation. The discrete cosine transform coefficient histograms of previously JPEG-compressed images exhibit near-periodic behavior due to quantization. We propose a statistical approach to exploit this structure and thereby estimate the images CH. Using simulations, we first demonstrate the accuracy of our estimation. Further, we show that JPEG recompression performed by exploiting the estimated CH strikes an excellent file-size versus distortion tradeoff.

Picture-graphics color image classification

High-level (semantic) image classification can be achieved by analysis of low-level image attributes geared for the particular classes. In this paper, we have proposed a novel application of the known image processing and classification techniques to achieve such a high-level classification of color images. Our image classification algorithm uses three low-level image features: texture, color, and edge characteristics to classify a color image into two classes: business graphics or natural picture. We have achieved an accuracy of 96.6% on our database of 209 images using a combination of tree and neural network classifiers.

Maximum likelihood estimation of JPEG quantization table in the identification of bitmap compression history

To process previously JPEG coded images the knowledge of the quantization table used in compression is sometimes required. This happens for example in JPEG artifact removal and in JPEG re-compression. However, the quantization table might not be known due to various reasons. A method is presented for the maximum likelihood estimation (MLE) of the JPEG quantization tables. An efficient method is also provided to identify if an image has been previously JPEG compressed.

Video-based real-time on-street parking occupancy detection system

Abstract. Urban parking management is receiving significant attention due to its potential to reduce traffic congestion, fuel consumption, and emissions. Real-time parking occupancy detection is a critical component of on-street parking management systems, where occupancy information is relayed to drivers via smart phone apps, radio, Internet, on-road signs, or global positioning system auxiliary signals. Video-based parking occupancy detection systems can provide a cost-effective solution to the sensing task while providing additional functionality for traffic law enforcement and surveillance. We present a video-based on-street parking occupancy detection system that can operate in real time. Our system accounts for the inherent challenges that exist in on-street parking settings, including illumination changes, rain, shadows, occlusions, and camera motion. Our method utilizes several components from video processing and computer vision for motion detection, background subtraction, and vehicle detection. We also present three traffic law enforcement applications: parking angle violation detection, parking boundary violation detection, and exclusion zone violation detection, which can be integrated into the parking occupancy cameras as a value-added option. Our experimental results show that the proposed parking occupancy detection method performs in real-time at 5  frames/s and achieves better than 90% detection accuracy across several days of videos captured in a busy street block under various weather conditions such as sunny, cloudy, and rainy, among others.

Limit cycle behavior of error diffusion

Error diffusion is an important technique for digital halftoning. One of the most observable artifacts of error diffusion is the periodically repeating patterns appearing in the areas where the input is constant or slowly varying. We analyze the limit cycle behavior of error diffusion, which explains the periodic oscillation under a constant input. We discuss why certain periodic patterns are more likely to appear than the others in the image. We demonstrate how to increase or reduce the occurrence of a pattern by manipulating the error diffusion weights.<<ETX>>