Showing papers on "Data compression published in 2008"

Imaging via Compressive Sampling

Abstract: Image compression algorithms convert high-resolution images into a relatively small bit streams in effect turning a large digital data set into a substantially smaller one. This article introduces compressive sampling and recovery using convex programming.

Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm

Abstract: As result of powerful image processing tools, digital image forgeries have already become a serious social problem. In this paper we describe an effective method to detect copy-move forgery in digital images. This method works by first extracting SIFT descriptors of an image, which are invariant to changes in illumination, rotation, scaling etc. Owing to the similarity between pasted region and copied region, descriptors are then matched between each other to seek for any possible forgery in images. Experiments have been performed to demonstrate the efficiency of this method on different forgeries and quantify its robustness and sensitivity to post image processing, such as additive noise and lossy JPEG compression etc, or even compound processing.

Rate Region of the Quadratic Gaussian Two-Encoder Source-Coding Problem

Abstract: We determine the rate region of the quadratic Gaussian two-encoder source-coding problem. This rate region is achieved by a simple architecture that separates the analog and digital aspects of the compression. Furthermore, this architecture requires higher rates to send a Gaussian source than it does to send any other source with the same covariance. Our techniques can also be used to determine the sum-rate of some generalizations of this classical problem. Our approach involves coupling the problem to a quadratic Gaussian ldquoCEO problem.rdquo

Compressed Sensing in Astronomy

Abstract: Recent advances in signal processing have focused on the use of sparse representations in various applications. A new field of interest based on sparsity has recently emerged: compressed sensing. This theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. In this paper, we investigate how compressed sensing (CS) can provide new insights into astronomical data compression. We first give a brief overview of the compressed sensing theory which provides very simple coding process with low computational cost, thus favoring its use for real-time applications often found onboard space mission. In practical situations, owing to particular observation strategies (for instance, raster scans) astronomical data are often redundant; in that context, we point out that a CS-based compression scheme is flexible enough to account for particular observational strategies. Indeed, we show also that CS provides a new fantastic way to handle multiple observations of the same field view, allowing us to recover low level details, which is impossible with standard compression methods. This kind of CS data fusion concept could lead to an elegant and effective way to solve the problem ESA is faced with, for the transmission to the earth of the data collected by PACS, one of the instruments onboard the Herschel spacecraft which will launched in late 2008/early 2009. We show that CS enables to recover data with a spatial resolution enhanced up to 30% with similar sensitivity compared to the averaging technique proposed by ESA.

Detection of Double-Compression in JPEG Images for Applications in Steganography

Abstract: This paper presents a method for the detection of double JPEG compression and a maximum-likelihood estimator of the primary quality factor. These methods are essential for construction of accurate targeted and blind steganalysis methods for JPEG images. The proposed methods use support vector machine classifiers with feature vectors formed by histograms of low-frequency discrete cosine transformation coefficients. The performance of the algorithms is compared to selected prior art.

Scrambling for Privacy Protection in Video Surveillance Systems

Abstract: In this paper, we address the problem of privacy protection in video surveillance. We introduce two efficient approaches to conceal regions of interest (ROIs) based on transform-domain or codestream-domain scrambling. In the first technique, the sign of selected transform coefficients is pseudorandomly flipped during encoding. In the second method, some bits of the codestream are pseudorandomly inverted. We address more specifically the cases of MPEG-4 as it is today the prevailing standard in video surveillance equipment. Simulations show that both techniques successfully hide private data in ROIs while the scene remains comprehensible. Additionally, the amount of noise introduced by the scrambling process can be adjusted. Finally, the impact on coding efficiency performance is small, and the required computational complexity is negligible.

Performance of compressed inverted list caching in search engines

Abstract: Due to the rapid growth in the size of the web, web search engines are facing enormous performance challenges. The larger engines in particular have to be able to process tens of thousands of queries per second on tens of billions of documents, making query throughput a critical issue. To satisfy this heavy workload, search engines use a variety of performance optimizations including index compression, caching, and early termination.We focus on two techniques, inverted index compression and index caching, which play a crucial rule in web search engines as well as other high-performance information retrieval systems. We perform a comparison and evaluation of several inverted list compression algorithms, including new variants of existing algorithms that have not been studied before. We then evaluate different inverted list caching policies on large query traces, and finally study the possible performance benefits of combining compression and caching. The overall goal of this paper is to provide an updated discussion and evaluation of these two techniques, and to show how to select the best set of approaches and settings depending on parameter such as disk speed and main memory cache size.

A Simple Algorithm for Data Compression in Wireless Sensor Networks

Abstract: Power saving is a critical issue in wireless sensor networks (WSNs) since sensor nodes are powered by batteries which cannot be generally changed or recharged. As radio communication is often the main cause of energy consumption, extension of sensor node lifetime is generally achieved by reducing transmissions/receptions of data, for instance through data compression. Exploiting the natural correlation that exists in data typically collected by WSNs and the principles of entropy compression, in this Letter we propose a simple and efficient data compression algorithm particularly suited to be used on available commercial nodes of a WSN, where energy, memory and computational resources are very limited. Some experimental results and comparisons with, to the best of our knowledge, the only lossless compression algorithm previously proposed in the literature to be embedded in sensor nodes and with two well- known compression algorithms are shown and discussed.

ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure

Abstract: This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For denosing, the SNR improvement criterion is used, while for compression, we have considered the compression ratio (CR), the percentage area difference (PAD), and the weighted diagnostic distortion (WDD) measure. Several Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG databases are used for performance evaluation. Simulation results illustrate that both applications can contribute to and enhance the clinical ECG data denoising and compression performance. For denoising, an average SNR improvement of 10.16 dB was achieved, which is 1.8 dB more than the next benchmark methods such as MAB WT or EKF2. For compression, the algorithm was extended to include more than five Gaussian kernels. Results show a typical average CR of 11.37:1 with WDD < 1.73 %. Consequently, the proposed framework is suitable for a hybrid system that integrates these algorithmic approaches for clean ECG data storage or transmission scenarios with high output SNRs, high CRs, and low distortions.

A Reversible Data Hiding Method for Encrypted Images

Abstract: Since several years, the protection of multimedia data is becoming very important The protection of this multimedia data can be done with encryption or data hiding algorithms To decrease the transmission time, the data compression is necessary Since few years, a new problem is trying to combine in a single step, compression, encryption and data hiding So far, few solutions have been proposed to combine image encryption and compression for example Nowadays, a new challenge consists to embed data in encrypted images Since the entropy of encrypted image is maximal, the embedding step, considered like noise, is not possible by using standard data hiding algorithms A new idea is to apply reversible data hiding algorithms on encrypted images by wishing to remove the embedded data before the image decryption Recent reversible data hiding methods have been proposed with high capacity, but these methods are not applicable on encrypted images In this paper we propose an analysis of the local standard deviation of the marked encrypted images in order to remove the embedded data during the decryption step We have applied our method on various images, and we show and analyze the obtained results

Robust Lossless Image Data Hiding Designed for Semi-Fragile Image Authentication

Abstract: Recently, among various data hiding techniques, a new subset, lossless data hiding, has received increasing interest. Most of the existing lossless data hiding algorithms are, however, fragile in the sense that the hidden data cannot be extracted out correctly after compression or other incidental alteration has been applied to the stego-image. The only existing semi-fragile (referred to as robust in this paper) lossless data hiding technique, which is robust against high-quality JPEG compression, is based on modulo-256 addition to achieve losslessness. In this paper, we first point out that this technique has suffered from the annoying salt-and-pepper noise caused by using modulo-256 addition to prevent overflow/underflow. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. By identifying a robust statistical quantity based on the patchwork theory and employing it to embed data, differentiating the bit-embedding process based on the pixel group's distribution characteristics, and using error correction codes and permutation scheme, this technique has achieved both losslessness and robustness. It has been successfully applied to many images, thus demonstrating its generality. The experimental results show that the high visual quality of stego-images, the data embedding capacity, and the robustness of the proposed lossless data hiding scheme against compression are acceptable for many applications, including semi-fragile image authentication. Specifically, it has been successfully applied to authenticate losslessly compressed JPEG2000 images, followed by possible transcoding. It is expected that this new robust lossless data hiding algorithm can be readily applied in the medical field, law enforcement, remote sensing and other areas, where the recovery of original images is desired.

A Generic Scheme for Progressive Point Cloud Coding

Abstract: In this paper, we propose a generic point cloud encoder that provides a unified framework for compressing different attributes of point samples corresponding to 3D objects with an arbitrary topology. In the proposed scheme, the coding process is led by an iterative octree cell subdivision of the object space. At each level of subdivision, the positions of point samples are approximated by the geometry centers of all tree-front cells, whereas normals and colors are approximated by their statistical average within each of the tree-front cells. With this framework, we employ attribute-dependent encoding techniques to exploit the different characteristics of various attributes. All of these have led to a significant improvement in the rate-distortion (R-D) performance and a computational advantage over the state of the art. Furthermore, given sufficient levels of octree expansion, normal space partitioning, and resolution of color quantization, the proposed point cloud encoder can be potentially used for lossless coding of 3D point clouds.

The Burrows-Wheeler Transform:: Data Compression, Suffix Arrays, and Pattern Matching

Abstract: The Burrows-Wheeler Transform is a text transformation scheme that has found applications in different aspects of the data explosion problem, from data compression to index structures and search. The BWT belongs to a new class of compression algorithms, distinguished by its ability to perform compression by sorted contexts. More recently, the BWT has also found various applications in addition to text data compression, such as in lossless and lossy image compression, tree-source identification, bioinformatics, machine translation, shape matching, and test data compression. This book will serve as a reference for seasoned professionals or researchers in the area, while providing a gentle introduction, making it accessible for senior undergraduate students or first year graduate students embarking upon research in compression, pattern matching, full text retrieval, compressed index structures, or other areas related to the BWT. Key Features Comprehensive resource for information related to different aspects of the Burrows-Wheeler Transform including: Gentle introduction to the BWT History of the development of the BWT Detailed theoretical analysis of algorithmic issues and performance limits Searching on BWT compressed data Hardware architectures for the BWT Explores non-traditional applications of the BWT in areas such as: Bioinformatics Joint source-channel coding Modern information retrieval Machine translation Test data compression for systems-on-chip Teaching materials ideal for classroom use on courses in: Data Compression and Source Coding Modern Information Retrieval Information Science Digital Libraries

Method and Apparatus for Block-Based Compression of Light-field Images

Abstract: A method and apparatus for the block-based compression of light-field images. Light-field images may be preprocessed by a preprocessing module into a format that is compatible with the blocking scheme of a block-based compression technique, for example JPEG. The compression technique is then used to compress the preprocessed light-field images. The light-field preprocessing module reshapes the angular data in a captured light-field image into shapes compatible with the blocking scheme of the compression technique so that blocking artifacts of block-based compression are not introduced in the final compressed image. Embodiments may produce compressed 2D images for which no specific light-field image viewer is needed to preview the full light-field image. Full light-field information is contained in one compressed 2D image.

Detecting doubly compressed JPEG images by using Mode Based First Digit Features

Abstract: In this paper, we utilize the probabilities of the first digits of quantized DCT (discrete cosine transform) coefficients from individual AC (alternate current) modes to detect doubly compressed JPEG images. Our proposed features, named by mode based first digit features (MBFDF), have been shown to outperform all previous methods on discriminating doubly compressed JPEG images from singly compressed JPEG images. Furthermore, combining the MBFDF with a multi-class classification strategy can be exploited to identify the quality factor in the primary JPEG compression, thus successfully revealing the double JPEG compression history of a given JPEG image.

Robust Video Fingerprinting for Content-Based Video Identification

Abstract: Video fingerprints are feature vectors that uniquely characterize one video clip from another. The goal of video fingerprinting is to identify a given video query in a database (DB) by measuring the distance between the query fingerprint and the fingerprints in the DB. The performance of a video fingerprinting system, which is usually measured in terms of pairwise independence and robustness, is directly related to the fingerprint that the system uses. In this paper, a novel video fingerprinting method based on the centroid of gradient orientations is proposed. The centroid of gradient orientations is chosen due to its pairwise independence and robustness against common video processing steps that include lossy compression, resizing, frame rate change, etc. A threshold used to reliably determine a fingerprint match is theoretically derived by modeling the proposed fingerprint as a stationary ergodic process, and the validity of the model is experimentally verified. The performance of the proposed fingerprint is experimentally evaluated and compared with that of other widely-used features. The experimental results show that the proposed fingerprint outperforms the considered features in the context of video fingerprinting.

Directional Discrete Cosine Transforms—A New Framework for Image Coding

Abstract: Nearly all block-based transform schemes for image and video coding developed so far choose the 2-D discrete cosine transform (DCT) of a square block shape. With almost no exception, this conventional DCT is implemented separately through two 1-D transforms, one along the vertical direction and another along the horizontal direction. In this paper, we develop a new block-based DCT framework in which the first transform may choose to follow a direction other than the vertical or horizontal one. The coefficients produced by all directional transforms in the first step are arranged appropriately so that the second transform can be applied to the coefficients that are best aligned with each other. Compared with the conventional DCT, the resulting directional DCT framework is able to provide a better coding performance for image blocks that contain directional edges-a popular scenario in many image signals. By choosing the best from all directional DCTs (including the conventional DCT as a special case) for each image block, we will demonstrate that the rate-distortion coding performance can be improved remarkably. Finally, a brief theoretical analysis is presented to justify why certain coding gain (over the conventional DCT) results from this directional framework.

System and method for data feed acceleration and encryption

Abstract: Systems and methods for providing accelerated transmission of broadcast data, such as financial data and news feeds, over a communication channel using data compression and decompression to provide secure transmission and transparent multiplication of communication bandwidth, as well as reduce the latency associated with data transmission of conventional systems.

Structural Similarity Quality Metrics in a Coding Context: Exploring the Space of Realistic Distortions

Abstract: Perceptual image quality metrics have explicitly accounted for human visual system (HVS) sensitivity to subband noise by estimating just noticeable distortion (JND) thresholds. A recently proposed class of quality metrics, known as structural similarity metrics (SSIM), models perception implicitly by taking into account the fact that the HVS is adapted for extracting structural information from images. We evaluate SSIM metrics and compare their performance to traditional approaches in the context of realistic distortions that arise from compression and error concealment in video compression/transmission applications. In order to better explore this space of distortions, we propose models for simulating typical distortions encountered in such applications. We compare specific SSIM implementations both in the image space and the wavelet domain; these include the complex wavelet SSIM (CWSSIM), a translation-insensitive SSIM implementation. We also propose a perceptually weighted multiscale variant of CWSSIM, which introduces a viewing distance dependence and provides a natural way to unify the structural similarity approach with the traditional JND-based perceptual approaches.

An ECG Signals Compression Method and Its Validation Using NNs

Abstract: This paper presents a new algorithm for electrocardiogram (ECG) signal compression based on local extreme extraction, adaptive hysteretic filtering and Lempel-Ziv-Welch (LZW) coding. The algorithm has been verified using eight of the most frequent normal and pathological types of cardiac beats and an multi-layer perceptron (MLP) neural network trained with original cardiac patterns and tested with reconstructed ones. Aspects regarding the possibility of using the principal component analysis (PCA) to cardiac pattern classification have been investigated as well. A new compression measure called ldquoquality score,rdquo which takes into account both the reconstruction errors and the compression ratio, is proposed.

Video compression and transmission techniques

Abstract: Embodiments feature families of rate allocation and rate control methods that utilize advanced processing of past and future frame/field picture statistics and are designed to operate with one or more coding passes. At least two method families include: a family of methods for a rate allocation with picture look-ahead; and a family of methods for average bit rate (ABR) control methods. At least two other methods for each method family are described. For the first family of methods, some methods may involve intra rate control. For the second family of methods, some methods may involve high complexity ABR control and/or low complexity ABR control. These and other embodiments can involve any of the following: spatial coding parameter adaptation, coding prediction, complexity processing, complexity estimation, complexity filtering, bit rate considerations, quality considerations, coding parameter allocation, and/or hierarchical prediction structures, among others.

Bandwidth sensitive data compression and decompression

Abstract: Data compression and decompression methods for compressing and decompressing data based on an actual or expected throughput (bandwidth) of a system. In one embodiment, a controller tracks and monitors the throughput (data storage and retrieval) of a data compression system and generates control signals to enable/disable different compression algorithms when, e.g., a bottleneck occurs so as to increase the throughput and eliminate the bottleneck.

Effect of Severe Image Compression on Iris Recognition Performance

Abstract: We investigate three schemes for severe compression of iris images in order to assess what their impact would be on recognition performance of the algorithms deployed today for identifying people by this biometric feature. Currently, standard iris images are 600 times larger than the IrisCode templates computed from them for database storage and search; but it is administratively desired that iris data should be stored, transmitted, and embedded in media in the form of images rather than as templates computed with proprietary algorithms. To reconcile that goal with its implications for bandwidth and storage, we present schemes that combine region-of-interest isolation with JPEG and JPEG2000 compression at severe levels, and we test them using a publicly available database of iris images. We show that it is possible to compress iris images to as little as 2000 bytes with minimal impact on recognition performance. Only some 2% to 3% of the bits in the IrisCode templates are changed by such severe image compression, and we calculate the entropy per code bit introduced by each compression scheme. Error tradeoff curve metrics document very good recognition performance despite this reduction in data size by a net factor of 150, approaching a convergence of image data size and template size.

Energy Minimization of Portable Video Communication Devices Based on Power-Rate-Distortion Optimization

Abstract: Portable video communication devices operate on batteries with limited energy supply. However, video compression is computationally intensive and energy-demanding. Therefore, one of the central challenging issues in portable video communication system design is to minimize the energy consumption of video encoding so as to prolong the operational lifetime of portable video devices. In this work, based on power-rate-distortion (P-R-D) optimization, we develop a new approach for energy minimization by exploring the energy tradeoff between video encoding and wireless communication and exploiting the nonstationary characteristics of input video data. Both analytically and experimentally, we demonstrate that incorporating the third dimension of power consumption into conventional R-D analysis gives us one extra dimension of flexibility in resource allocation and allows us to achieve significant energy saving. Within the P-R-D analysis framework, power is tightly coupled with rate, enabling us to trade bits for joules and perform energy minimization through optimum bit allocation. We analyze the energy saving gain of P-R-D optimization. We develop an adaptive scheme to estimate P-R-D model parameters and perform online resource allocation and energy optimization for real-time video encoding. Our experimental studies show that, for typical videos with nonstationary scene statistics, using the proposed P-R-D optimization technology, the energy consumption of video encoding can be significantly reduced (by up to 50%), especially in delay-tolerant portable video communication applications.

Modeling Background and Segmenting Moving Objects from Compressed Video

Abstract: Modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. Most existing methods of modeling background and segmenting moving objects mainly operate in the spatial domain at pixel level. In this paper, we present three new algorithms (running average, median, mixture of Gaussians) modeling background directly from compressed video, and a two-stage segmentation approach based on the proposed background models. The proposed methods utilize discrete cosine transform (DCT) coefficients (including ac coefficients) at block level to represent background, and adapt the background by updating DCT coefficients. The proposed segmentation approach can extract foreground objects with pixel accuracy through a two-stage process. First a new background subtraction technique in the DCT domain is exploited to identify the block regions fully or partially occupied by foreground objects, and then pixels from these foreground blocks are further classified in the spatial domain. The experimental results show the proposed background modeling algorithms can achieve comparable accuracy to their counterparts in the spatial domain, and the associated segmentation scheme can visually generate good segmentation results with efficient computation. For instance, the computational cost of the proposed median and MoG algorithms are only 40.4% and 20.6% of their counterparts in the spatial domain for background construction.

Imaging via Compressive Sampling [Introduction to compressive sampling and recovery via convex programming]

Abstract: There is an extensive body of literature on image compression, but the central concept is straightforward: we transform the image into an appropriate basis and then code only the important expansion coefficients. The crux is finding a good transform, a problem that has been studied extensively from both a theoretical [14] and practical [25] standpoint. The most notable product of this research is the wavelet transform [9], [16]; switching from sinusoid-based representations to wavelets marked a watershed in image compression and is the essential difference between the classical JPEG [18] and modern JPEG-2000 [22] standards. Image compression algorithms convert high-resolution images into a relatively small bit streams (while keeping the essential features intact), in effect turning a large digital data set into a substantially smaller one. But is there a way to avoid the large digital data set to begin with? Is there a way we can build the data compression directly into the acquisition? The answer is yes, and is what compressive sampling (CS) is all about.

Hyperspectral Image Compression: Adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

Abstract: Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties.

Lifting-based reversible color transformations for image compression

Abstract: This paper reviews a set of color spaces that allow reversible mapping between red-green-blue and luma-chroma representations in integer arithmetic. The YCoCg transform and its reversible form YCoCg-R can improve coding gain by over 0.5 dB with respect to the popular YCrCb transform, while achieving much lower computational complexity. We also present extensions of the YCoCg transform for four-channel CMYK pixel data. Thanks to their reversibility under integer arithmetic, these transforms are useful for both lossy and lossless compression. Versions of these transforms are used in the HD Photo image coding technology (which is the basis for the upcoming JPEG XR standard) and in recent editions of the H.264/MPEG-4 AVC video coding standard. Keywords: Image coding, color transforms, lossless coding, YCoCg, JPEG, JPEG XR, HD Photo. 1. INTRODUCTION In color image compression, usually the input image has three color values per pixel: red, green, and blue (RGB). Independent compression of each of the R, G, and B color planes is possible (and explicitly allowed in standards such as JPEG 2000

Flowfield motion compensation for video compression

Abstract: Motion compensation for video compression using a 'flowfield' comprising a per-pixel field of motion vectors and confidence values. Flowfields can be quantized transform coded for compression motion compensation. Encoding-only flowfields match with one or more previous and subsequent frames to determine both modulation for resolution- enhancing layers, as well as sharp/soft filtering for an original image, a base layer, and for resolution-enhancing layers. Encoding-only flowfields can be used with various codec types by using the flowfield motion vector length and confidence to drive sharp/soft filters to improve efficiency via in-place noise reduction. Pixels may be displaced using encoding-only flowfields to nearby frames, and weighted for efficient noise reduction. Encoding-only flowfields are discarded after their use in encoding, and therefore do not require coded bits. Encoding-only flowfields can be applied to all frame types, including intra, predicted, forward flowfield-predicted 'F' frames, and multiply-predicted 'M' frame types, and improve intra coding efficiency.

Efficient Image Deblocking Based on Postfiltering in Shifted Windows

Abstract: We propose a simple yet effective deblocking method for JPEG compressed image through postfiltering in shifted windows (PSW) of image blocks. The MSE is compared between the original image block and the image blocks in shifted windows, so as to decide whether these altered blocks are used in the smoothing procedure. Our research indicates that there exists strong correlation between the optimal mean squared error threshold and the image quality factor Q, which is selected in the encoding end and can be computed from the quantization table embedded in the JPEG file. Also we use the standard deviation of each original block to adjust the threshold locally so as to avoid the over-smoothing of image details. With various image and bit-rate conditions, the processed image exhibits both great visual effect improvement and significant peak signal-to-noise ratio gain with fairly low computational complexity. Extensive experiments and comparison with other deblocking methods are conducted to justify the effectiveness of the proposed PSW method in both objective and subjective measures.