Showing papers on "Quantization (image processing) published in 2009"

Exposing Digital Forgeries From JPEG Ghosts

Abstract: When creating a digital forgery, it is often necessary to combine several images, for example, when compositing one person's head onto another person's body. If these images were originally of different JPEG compression quality, then the digital composite may contain a trace of the original compression qualities. To this end, we describe a technique to detect whether the part of an image was initially compressed at a lower quality than the rest of the image. This approach is applicable to images of high and low quality as well as resolution.

Packing bag-of-features

Abstract: One of the main limitations of image search based on bag-of-features is the memory usage per image Only a few million images can be handled on a single machine in reasonable response time In this paper, we first evaluate how the memory usage is reduced by using lossless index compression We then propose an approximate representation of bag-of-features obtained by projecting the corresponding histogram onto a set of pre-defined sparse projection functions, producing several image descriptors Coupled with a proper indexing structure, an image is represented by a few hundred bytes A distance expectation criterion is then used to rank the images Our method is at least one order of magnitude faster than standard bag-of-features while providing excellent search quality

Exposing digital forgeries in video by detecting double quantization

Abstract: We describe a technique for detecting double quantization in digital video that results from double MPEG compression or from combining two videos of different qualities (e.g., green-screening). We describe how double quantization can introduce statistical artifacts that while not visible, can be quantified, measured, and used to detect tampering. This technique can detect highly localized tampering in regions as small as 16 x 16 pixels.

The JPEG XR image coding standard [Standards in a Nutshell]

Abstract: JPEG XR is the newest image coding standard from the JPEG committee. It primarily targets the representation of continuous-tone still images such as photographic images and achieves high image quality, on par with JPEG 2000, while requiring low computational resources and storage capacity. Moreover, it effectively addresses the needs of emerging high dynamic range imagery applications by including support for a wide range of image representation formats.

Energy-Efficient Image Compression for Resource-Constrained Platforms

Abstract: One of the most important goals of current and future sensor networks is energy-efficient communication of images. This paper presents a quantitative comparison between the energy costs associated with 1) direct transmission of uncompressed images and 2) sensor platform-based JPEG compression followed by transmission of the compressed image data. JPEG compression computations are mapped onto various resource-constrained platforms using a design environment that allows computation using the minimum integer and fractional bit-widths needed in view of other approximations inherent in the compression process and choice of image quality parameters. Advanced applications of JPEG, such as region of interest coding and successive/progressive transmission, are also examined. Detailed experimental results examining the tradeoffs in processor resources, processing/transmission time, bandwidth utilization, image quality, and overall energy consumption are presented.

Fast macroblock delta qp decision

Abstract: A system and method for encoding multimedia video is described. As video is encoded a quantization parameter is selected for each macroblock. As described herein, the quantization parameter for each macroblock may be selected by limiting the universe of all possible quantization parameters to a particular range of possible quantization parameter values. This increases the speed of video encoding by reducing the number of quantization parameters that are tested for each video macroblock.

The JPEG 2000 Suite

Abstract: Contributor Biographies. Foreword. Series Editor's Preface. Preface. Acknowledgments. List of Acronyms. Part A. 1 JPEG 2000 Core Coding System (Part 1) ( Majid Rabbani, Rajan L. Joshi, and Paul W. Jones ). 1.1 Introduction. 1.2 JPEG 2000 Fundamental Building Blocks. 1.3 JPEG 2000 Bit-Stream Organization. 1.4 JPEG 2000 Rate Control. 1.5 Performance Comparison of the JPEG 2000 Encoder Options. 1.6 Additional Features of JPEG 2000 Part 1. Acknowledgments. References. 2 JPEG 2000 Extensions (Part 2) ( Margaret Lepley, J. Scott Houchin, James Kasner, and Michael Marcellin ). 2.1 Introduction. 2.2 Variable DC Offset. 2.3 Variable Scalar Quantization. 2.4 Trellis-Coded Quantization. 2.5 Precinct-Dependent Quantization. 2.6 Extended Visual Masking. 2.7 Arbitrary Decomposition. 2.8 Arbitrary Wavelet Transforms. 2.9 Multiple-Component Transform Extensions. 2.10 Nonlinear Point Transform. 2.11 Geometric Manipulation via a Code-Block Anchor Point (CBAP). 2.12 Single-Sample Overlap. 2.13 Region of Interest. 2.14 Extended File Format: JPX. 2.15 Extended Capabilities Signaling. Acknowledgments. References. 3 Motion JPEG 2000 and ISO Base Media File Format (Parts 3 and 12) ( Joerg Mohr ). 3.1 Introduction. 3.2 Motion JPEG 2000 and ISO Base Media File Format. 3.3 ISO Base Media File Format. 3.4 Motion JPEG 2000. References. 4 Compound Image File Format (Part 6) ( Frederik Temmermans, Tim Bruylants, Simon McPartlin, and Louis Sharpe ). 4.1 Introduction. 4.2 The JPM File Format. 4.3 Mixed Raster Content Model (MRC). 4.4 Streaming JPM Files. 4.5 Referencing JPM Files. 4.6 Metadata. 4.7 Boxes. 4.8 Profiles. 4.9 Conclusions. References. 5 JPSEC: Securing JPEG 2000 Files (Part 8) ( Susie Wee and Zhishou Zhang ). 5.1 Introduction. 5.2 JPSEC Security Services. 5.3 JPSEC Architecture. 5.4 JPSEC Framework. 5.5 What: JPSEC Security Services. 5.6 Where: Zone of Influence (ZOI). 5.7 How: Processing Domain and Granularity. 5.8 JPSEC Examples. 5.9 Summary. References. 6 JPIP - Interactivity Tools, APIs, and Protocols (Part 9) ( Robert Prandolini ). 6.1 Introduction. 6.2 Data-Bins. 6.3 JPIP Basics. 6.4 Client Request-Server Response. 6.5 Advanced Topics. 6.6 Conclusions. Acknowledgments. References. 7 JP3D - Extensions for Three-Dimensional Data (Part 10) ( Tim Bruylants, Peter Schelkens, and Alexis Tzannes ). 7.1 Introduction. 7.2 JP3D: Going Volumetric. 7.3 Bit-Stream Organization. 7.4 Additional Features of JP3D. 7.5 Compression performances: JPEG 2000 Part 1 versus JP3D. 7.6 Implications for Other Parts of JPEG 2000. Acknowledgments. References. 8 JPWL - JPEG 2000 Wireless (Part 11) ( Frederic Dufaux ). 8.1 Introduction. 8.2 Background. 8.3 JPWL Overview. 8.4 Normative Parts. 8.5 Informative Parts. 8.6 Summary. Acknowledgments. References. Part B. 9 JPEG 2000 for Digital Cinema ( Siegfried F o ssel ). 9.1 Introduction. 9.2 General Requirements for Digital Cinema. 9.3 Distribution of Digital Cinema Content. 9.4 Archiving of Digital Movies. 9.5 Future Use of JPEG 2000 within Digital Cinema. 9.6 Conclusions. Acknowledgments. References. 10 Security Applications for JPEG 2000 Imagery ( John Apostolopoulos, Frederic Dufaux, and Qibin Sun ). 10.1 Introduction. 10.2 Secure Transcoding and Secure Streaming. 10.3 Multilevel Access Control. 10.4 Selective or Partial Encryption of Image Content. 10.5 Image Authentication. 10.6 Summary. Acknowledgments. References. 11 Video Surveillance and Defense Imaging ( Touradj Ebrahimi and Frederic Dufaux ). 11.1 Introduction. 11.2 Scrambling. 11.3 Overview of a Typical Video Surveillance System. 11.4 Overview of a Video Surveillance System Based on JPEG 2000 and ROI Scrambling. 12 JPEG 2000 Application in GIS and Remote Sensing ( Bernard Brower, Robert Fiete, and Roddy Shuler ). 12.1 Introduction. 12.2 Geographic Information Systems. 12.3 Recommendations for JPEG 2000 Encoding. 12.4 Other JPEG 2000 Parts to Consider. References. 13 Medical Imaging ( Alexis Tzannes and Ron Gut ). 13.1 Introduction. 13.2 Background. 13.3 DICOM and JPEG 2000 Part 1. 13.4 DICOM and JPEG 2000 Part 2. 13.5 Example Results. 13.6 Image Streaming, DICOM, and JPIP. References. 14 Digital Culture Imaging ( Greg Colyer, Robert Buckley, and Athanassios Skodras ). 14.1 Introduction. 14.2 The Digital Culture Context. 14.3 Digital Culture and JPEG 2000. 14.4 Application - National Digital Newspaper Program. Acknowledgments. References. 15 Broadcast Applications ( Hans Hoffman, Adi Kouadio, and Luk Overmeire ). 15.1 Introduction - From Tape-Based to File-Based Production. 15.2 Broadcast Production Chain Reference Model. 15.3 Codec Requirements for Broadcasting Applications. 15.4 Overview of State-of-the-Art HD Compression Schemes. 15.5 JPEG 2000 Applications. 15.6 Multigeneration Production Processes. 15.7 JPEG 2000 Comparison with SVC. 15.8 Conclusion. References. 16 JPEG 2000 in 3-D Graphics Terrain Rendering ( Gauthier Lafruit, Wolfgang Van Raemdonck, Klaas Tack, and Eric Delfosse ). 16.1 Introduction. 16.2 Tiling: The Straightforward Solution to Texture Streaming. 16.3 View-Dependent JPEG 2000 Texture Streaming and Mipmapping. 16.4 JPEG 2000 Quality and Decoding Time Scalability for Optimal Quality-Workload Tradeoff. 16.5 Conclusion. References. 17 Conformance Testing, Reference Software, and Implementations ( Peter Schelkens, Yiannis Andreopoulos, and Joeri Barbarien ). 17.1 Introduction. 17.2 Part 4 - Conformance Testing. 17.3 Part 5 - Reference Software. 17.4 Implementation of the Discrete Wavelet Transform as Suggested by the JPEG 2000 Standard. 17.5 JPEG 2000 Hardware and Software Implementations. 17.6 Conclusions. Acknowledgments. References. 18 Ongoing Standardization Efforts ( Touradj Ebrahimi, Athanassios Skodras, and Peter Schelkens ). 18.1 Introduction. 18.2 JPSearch. 18.3 JPEG XR. 18.4 Advanced Image Coding and Evaluation Methodologies (AIC). References. Index.

Joint Optimization of Run-Length Coding, Huffman Coding, and Quantization Table With Complete Baseline JPEG Decoder Compatibility

Abstract: To maximize rate distortion performance while remaining faithful to the JPEG syntax, the joint optimization of the Huffman tables, quantization step sizes, and DCT indices of a JPEG encoder is investigated. Given Huffman tables and quantization step sizes, an efficient graph-based algorithm is first proposed to find the optimal DCT indices in the form of run-size pairs. Based on this graph-based algorithm, an iterative algorithm is then presented to jointly optimize run-length coding, Huffman coding, and quantization table selection. The proposed iterative algorithm not only results in a compressed bitstream completely compatible with existing JPEG and MPEG decoders, but is also computationally efficient. Furthermore, when tested over standard test images, it achieves the best JPEG compression results, to the extent that its own JPEG compression performance even exceeds the quoted PSNR results of some state-of-the-art wavelet-based image coders such as Shapiro's embedded zerotree wavelet algorithm at the common bit rates under comparison. Both the graph-based algorithm and the iterative algorithm can be applied to application areas such as web image acceleration, digital camera image compression, MPEG frame optimization, and transcoding, etc.

Lossy compression of encrypted image by compressive sensing technique

Abstract: The problem of lossy compression of encrypted image data is considered in this paper. A method is proposed to achieve compression of the encrypted image data based on com-pressive sensing technique. Joint decoding/decryption is proposed with the modified basis pursuit decoding method to take care of encryption. Simulation results are provided to demonstrate the compression results of the proposed compression method based on compressive sensing.

Beating the Quality of JPEG 2000 with Anisotropic Diffusion

Abstract: Although widely used standards such as JPEG and JPEG 2000 exist in the literature, lossy image compression is still a subject of ongoing research. Galic et al. (2008) have shown that compression based on edge-enhancing anisotropic diffusion can outperform JPEG for medium to high compression ratios when the interpolation points are chosen as vertices of an adaptive triangulation. In this paper we demonstrate that it is even possible to beat the quality of the much more advanced JPEG 2000 standard when one uses subdivisions on rectangles and a number of additional optimisations. They include improved entropy coding, brightness rescaling, diffusivity optimisation, and interpolation swapping. Experiments on classical test images are presented that illustrate the potential of our approach.

Image matching in large scale indoor environment

Abstract: In this paper, we propose a data driven approach to first-person vision. We propose a novel image matching algorithm, named Re-Search, that is designed to cope with self-repetitive structures and confusing patterns in the indoor environment. This algorithm uses state-of-art image search techniques, and it matches a query image with a two-pass strategy. In the first pass, a conventional image search algorithm is used to search for a small number of images that are most similar to the query image. In the second pass, the retrieval results from the first step are used to discover features that are more distinctive in the local context. We demonstrate and evaluate the Re-Search algorithm in the context of indoor localization, with the illustration of potential applications in object pop-out and data-driven zoom-in.

An ultra-low-energy/frame multi-standard JPEG co-processor in 65nm CMOS with sub/near-threshold power supply

Abstract: Many digital ICs can benefit from sub/near threshold operations that provide ultra-low-energy/operation for long battery lifetime. In addition, sub/near threshold operation largely mitigates the transient current hence lowering the ground bounce noise. This also helps to improve the performance of sensitive analog circuits on the chip, such as delay-lock loops (DLL), which is crucial for the functioning of large digital circuits. However, aggressive voltage scaling causes throughput and reliability degradation. This paper presents SubJPEG, a state of the art multi-standard 65nm CMOS JPEG encoding coprocessor that enables ultra-wide V DD scaling. With a 0.45V power supply, it delivers 15fps 640×480 VGA application with only 1.3pJ/operation energy consumption per DCT and quantization computation. This co-processor is very suitable for applications such as digital cameras, portable wireless and medical imaging. To the best of our knowledge, this is the largest sub-threshold processor so far.

Methods for local phase quantization in blur-insensitive image analysis

Abstract: Image quality is often degraded by blur caused by, for example, misfocused optics or camera motion. Blurring may also deteriorate the performance of computer vision algorithms if the image features computed are sensitive to these degradations. In this paper, we present an image descriptor based on local phase quantization that is robust to centrally symmetric blur. The descriptor referred to as local phase quantization (LPQ) can be used to characterize the underlying image texture. We also present a decorrelation scheme and propose three approaches for extracting the local phase information. Different combinations of them result in totally six variants of the operator that can be used alternatively. We show experimentally that these operators have slightly varying performance under different blurring conditions. In all test cases, including also sharp images, the new descriptors can outperform two state-of-the-art methods, namely, local binary pattern (LBP) and a method based on Gabor filter banks.

Digital forgery estimation into DCT domain: a critical analysis

Abstract: One of the key characteristics of digital images with a discrete representation is its pliability to manipulation. Recent trends in the field of unsupervised detection of digital forgery includes several advanced strategies devoted to reveal anomalies just considering several aspects of multimedia content. One of the promising approach, among others, considers the possibility to exploit the statistical distribution of DCT coefficients in order to reveal the irregularities due to the presence of a superimposed signal over the original one (e.g., copy and paste). As recently proved the ratio between the quantization tables used to compress the signal before and after the malicious forgery alter the histograms of the DCT coefficients especially for some basis that are close in terms of frequency content. In this work we analyze in more details the performances of existing approaches evaluating their effectiveness by making use of different input datasets with respect to resolution size, compression ratio and just considering different kind of forgeries (e.g., presence of duplicate regions or images composition). We also present possible post-processing techniques able to manipulate the forged image just to reduce the performance of the current state-of-art solution. Finally we conclude the papers providing future improvements devoted to increase robustness and reliability of forgery detection into DCT domain.

A psychophysical evaluation of inverse tone mapping techniques

Abstract: In recent years inverse tone mapping techniques have been proposed for enhancing low-dynamic range (LDR) content for a high-dynamic range (HDR) experience on HDR displays, and for image based lighting. In this paper, we present a psychophysical study to evaluate the performance of inverse (reverse) tone mapping algorithms. Some of these techniques are computationally expensive because they need to resolve quantization problems that can occur when expanding an LDR image. Even if they can be implemented efficiently on hardware, the computational cost can still be high. An alternative is to utilize less complex operators; although these may suffer in terms of accuracy. Our study investigates, firstly, if a high level of complexity is needed for inverse tone mapping and, secondly, if a correlation exists between image content and quality. Two main applications have been considered: visualization on an HDR monitor and image-based lighting.

JPEG Steganography: A Performance Evaluation of Quantization Tables

Abstract: The two most important aspects of any image based steganographic system are the imperceptibility and the capacity of the stego image. This paper evaluates the performance and efficiency of using optimized quantization tables instead of default JPEG tables within JPEG steganography. We found that using optimized tables significantly improves the quality of stego-images. Moreover, we used this optimization strategy to generate a 16x16 quantization table to be used instead of that suggested. The quality of stego-images was greatly improved when these optimized tables were used. This led us to suggest a new hybrid steganographic method in order to increase the embedding capacity. This new method is based on both and Jpeg-Jsteg methods. In this method, for each 16x16 quantized DCT block, the least two significant bits (2-LSBs) of each middle frequency coefficient are modified to embed two secret bits. Additionally, the Jpeg-Jsteg embedding technique is used for the low frequency DCT coefficients without modifying the DC coefficient. Our experimental results show that the proposed approach can provide a higher information-hiding capacity than the other methods tested. Furthermore, the quality of the produced stego-images is better than that of other methods which use the default tables.

An improved scaled DCT architecture

Abstract: This paper presents an efficient architecture for computing the eight-point 1D scaled DCT (discrete cosine transform) with a new algorithm based on a selected Loeffler DCT scheme whose multiplications are placed in the last stage. The proposed DCT architecture does not require any scaling compensation in the computation. Furthermore, a multiplication approximation method is developed, which is more efficient than traditional CORDIC (coordinate rotation digital computer)-based algorithms. Compared to the latest work (Sun et al., 2007), the proposed approach can save 14% addition operations for the same precision requirement and the path delay can be significantly reduced as well.

Evolutionary optimization of JPEG quantization tables for compressing iris polar images in iris recognition systems

Abstract: Recognition performance in iris biometrics strongly depends on the image quality. The appliance of compression algorithms to iris images raises the question whether it is possible to adapt those algorithms for biometrical purposes. In this work, we propose customized JPEG quantization matrices for compressing iris polar images to positively impact the recognition performance. We build on previous research and apply a genetic algorithm to obtain specialized matrices for destined compression ratios. The proposed tables are able to clearly outperform JPEG's standard quantization matrix. Moreover, some matrices also provide superior results in terms of ROC characteristics as compared to the reference scenario using uncompressed images. This leads to clearly lower error rates while also significantly reducing the necessary amount of data storage and transmission.

Multiscale Probabilistic Dithering for Suppressing Contour Artifacts in Digital Images

Abstract: A method is proposed for reducing the visibility of ldquocontour artifacts,rdquo i.e., false contours resulting from color quantization in digital images. The method performs a multiscale analysis on the neighborhood of each pixel, determines the presence and scale of contour artifacts, and probabilistically dithers (perturbs) the color of the pixel. The overall effect is to ldquobreak downrdquo the false contours, making them less visible. The proposed method may be used to reduce contour artifacts at the same bit depth as the input image or at higher bit depths. The contour artifact detection mechanism ensures that artifact-free regions remain unaffected during the process.

Improved detection and evaluation for JPEG steganalysis

Abstract: Detection of information-hiding in JPEG images is actively delivered in steganalysis community due to the fact that JPEG is a widely used compression standard and several steganographic systems have been designed for covert communication in JPEG images. In this paper, we propose a novel method of JPEG steganalysis. Based on an observation of bi-variate generalized Gaussian distribution in Discrete Cosine Transform (DCT) domain, neighboring joint density features on both intra-block and inter-block are extracted. Support Vector Machines (SVMs) are applied for detection. Experimental results indicate that this new method prominently improves a current art of steganalysis in detecting several steganographic systems in JPEG images. Our study also shows that it is more accurate to evaluate the detection performance in terms of both image complexity and information hiding ratio.

Encoding and decoding architecture of checkerboard multiplexed image data

Abstract: A device includes a coder or a codec configured for interleaved image data utilizing diamond shaped blocks for motion estimation and/or motion compensation and utilizing square or orthogonal transforms of residual data. In various embodiments, the decoder may be configured, among others, to perform de-blocking on edges of the diamond shaped blocks and/or data padding at boundaries of the image data. Additionally a method is proposed in which at least one of a transform and quantization process to be applied to de-multiplexed data is modified. One application is to combine left and right stereoscopie images, interleaved in a checkerboard manner.

A Document Image Model and Estimation Algorithm for Optimized JPEG Decompression

Abstract: The JPEG standard is one of the most prevalent image compression schemes in use today. While JPEG was designed for use with natural images, it is also widely used for the encoding of raster documents. Unfortunately, JPEG's characteristic blocking and ringing artifacts can severely degrade the quality of text and graphics in complex documents. We propose a JPEG decompression algorithm which is designed to produce substantially higher quality images from the same standard JPEG encodings. The method works by incorporating a document image model into the decoding process which accounts for the wide variety of content in modern complex color documents. The method works by first segmenting the JPEG encoded document into regions corresponding to background, text, and picture content. The regions corresponding to text and background are then decoded using maximum a posteriori (MAP) estimation. Most importantly, the MAP reconstruction of the text regions uses a model which accounts for the spatial characteristics of text and graphics. Our experimental comparisons to the baseline JPEG decoding as well as to three other decoding schemes, demonstrate that our method substantially improves the quality of decoded images, both visually and as measured by PSNR.

A new approach for JPEG resize and image splicing detection

Abstract: Today's ubiquitous digital media are easily tampered by, e.g., removing or adding objects from or into images without leaving any obvious clues. JPEG is a most widely used standard in digital images and it can be easily doctored. It is therefore necessary to have reliable methods to detect forgery in JPEG images for applications in law enforcement, forensics, etc. In this paper, based on the correlation of neighboring Discrete Cosine Transform (DCT) coefficients, we propose a method to detect resized JPEG images and spliced images, which are widely used in image forgery. In detail, the neighboring joint density features of the DCT coefficients are extracted; then Support Vector Machines (SVM) are applied to the features for detection. To improve the evaluation of JPEG resized detection, we utilize the shape parameter of generalized Gaussian distribution (GGD) of DCT coefficients to measure the image complexity.The study shows that our method is highly effective in detecting JPEG images resizing and splicing forgery. In the detection of resized JPEG image, the performance is related to both image complexity and resize scale factor. At the same scale factor, the detection performance in high image complexity is, as can be expected, lower than that in low image complexity.

Levels of details for gaussian mixture models

Abstract: Mixtures of Gaussians are a crucial statistical modeling tool at the heart of many challenging applications in computer vision and machine learning. In this paper, we first describe a novel and efficient algorithm for simplifying Gaussian mixture models using a generalization of the celebrated k-means quantization algorithm tailored to relative entropy. Our method is shown to compare experimentally favourably well with the state-of-the-art both in terms of time and quality performances. Second, we propose a practical enhanced approach providing a hierarchical representation of the simplified GMM while automatically computing the optimal number of Gaussians in the simplified mixture. Application to clustering-based image segmentation is reported.

Vocabulary hierarchy optimization for effective and transferable retrieval

Abstract: Scalable image retrieval systems usually involve hierarchical quantization of local image descriptors, which produces a visual vocabulary for inverted indexing of images. Although hierarchical quantization has the merit of retrieval efficiency, the resulting visual vocabulary representation usually faces two crucial problems: (1) hierarchical quantization errors and biases in the generation of “visual words”; (2) the model cannot adapt to database variance. In this paper, we describe an unsupervised optimization strategy in generating the hierarchy structure of visual vocabulary, which produces a more effective and adaptive retrieval model for large-scale search. We adopt a novel density-based metric learning (DML) algorithm, which corrects word quantization bias without supervision in hierarchy optimization, based on which we present a hierarchical rejection chain for efficient online search based on the vocabulary hierarchy. We also discovered that by hierarchy optimization, efficient and effective transfer of a retrieval model across different databases is feasible. We deployed a large-scale image retrieval system using a vocabulary tree model to validate our advances. Experiments on UKBench and street-side urban scene databases demonstrated the effectiveness of our hierarchy optimization approach in comparison with state-of-the-art methods.

Universal fixed-pixel-size ISP scheme

Abstract: An Image Signal Processing unit (ISP) has at least one fixed-size line buffer which is smaller than the width of the image buffer. To handle the image data, the image buffer is divided into regions which are sequentially loaded into the at least one fixed-size line buffer of the ISP for processing. Since functions of the ISP operate with neighboring pixels of the target pixel, margins of the regions need to be transmitted as well. After processing by the ISP, the data is encoded which includes a DCT, Quantization, and VLC. The result is then stored in segments in a buffer storage. VLC also inserts a Restart Marker which is used as a pointer to stitch together all the segments thus producing a new and seamless image.

Custom JPEG Quantization for Improved Iris Recognition Accuracy

Abstract: Custom JPEG quantization matrices are proposed to be used in the context of compression within iris recognition. Superior matching results in terms of average Hamming distance and improved ROC is found as compared to the use of the default quantization table especially for low FAR. This leads to improved user convenience in case high security is required.

An efficient Image Retrieval through DCT Histogram Quantization

Abstract: This paper proposes a new simple method of Discrete Cosine Transform (DCT) feature extraction that is used to accelerate the speed and decrease the storage needed in the image retrieving process. Image features are accessed and extracted directly from JPEG compressed domain. This method extracts and constructs a feature vector of histogram quantization from partial DCT coefficient in order to count the number of coefficients that have the same DCT coefficient over all image blocks. The database image and query image is equally divided into a non overlapping 8X8 block pixel, each of which is associated with a feature vector of histogram quantization derived directly from discrete cosine transform DCT. Users can select any query as the main theme of the query image. The retrieved images are those from the database that bear close resemblance with the query image and the similarity is ranked according to the closest similar measures computed by the Euclidean distance. The experimental results are significant and promising and show that our approach can easily identify main objects while to some extent reducing the influence of background in the image and in this way improves the performance of image retrieval.