The limitations of commonly used separable extensions of one-dimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. In this paper, we pursue a "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information. The main challenge in exploring geometry in images comes from the discrete nature of the data. Thus, unlike other approaches, such as curvelets, that first develop a transform in the continuous domain and then discretize for sampled data, our approach starts with a discrete-domain construction and then studies its convergence to an expansion in the continuous domain. Specifically, we construct a discrete-domain multiresolution and multidirection expansion using nonseparable filter banks, in much the same way that wavelets were derived from filter banks. This construction results in a flexible multiresolution, local, and directional image expansion using contour segments, and, thus, it is named the contourlet transform. The discrete contourlet transform has a fast iterated filter bank algorithm that requires an order N operations for N-pixel images. Furthermore, we establish a precise link between the developed filter bank and the associated continuous-domain contourlet expansion via a directional multiresolution analysis framework. We show that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves. Finally, we show some numerical experiments demonstrating the potential of contourlets in several image processing applications.

/pdf/the-contourlet-transform-an-efficient-directional-bbl3m3gyb1.pdf

The contourlet transform: an efficient directional multiresolution image representation

Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.

/pdf/frequency-tuned-salient-region-detection-4po18ylvds.pdf

Frequency-tuned salient region detection

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

https://mmcheng.net/mftp/SalObj/PosterSaliency.pdf

Global contrast based salient region detection

The paper discusses the theory behind the dual-tree transform, shows how complex wavelets with good properties can be designed, and illustrates a range of applications in signal and image processing The authors use the complex number symbol C in CWT to avoid confusion with the often-used acronym CWT for the (different) continuous wavelet transform The four fundamentals, intertwined shortcomings of wavelet transform and some solutions are also discussed Several methods for filter design are described for dual-tree CWT that demonstrates with relatively short filters, an effective invertible approximately analytic wavelet transform can indeed be implemented using the dual-tree approach

/pdf/the-dual-tree-complex-wavelet-transform-4kfm5jwrrq.pdf

The dual-tree complex wavelet transform

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

One of the aims of the standardization committee has been the development of Part I, which could be used on a royalty- and fee-free basis. This is important for the standard to become widely accepted. The standardization process, which is coordinated by the JTCI/SC29/WG1 of the ISO/IEC has already produced the international standard (IS) for Part I. In this article the structure of Part I of the JPFG 2000 standard is presented and performance comparisons with established standards are reported. This article is intended to serve as a tutorial for the JPEG 2000 standard. The main application areas and their requirements are given. The architecture of the standard follows with the description of the tiling, multicomponent transformations, wavelet transforms, quantization and entropy coding. Some of the most significant features of the standard are presented, such as region-of-interest coding, scalability, visual weighting, error resilience and file format aspects. Finally, some comparative results are reported and the future parts of the standard are discussed.

/pdf/the-jpeg-2000-still-image-compression-standard-2dd3aimci6.pdf

The JPEG 2000 still image compression standard

With the increasing use of multimedia technologies, image compression requires higher performance as well as new features. To address this need in the specific area of still image encoding, a new standard is currently being developed, the JPEG2000. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide features and functionalities that current standards can either not address efficiently or in many cases cannot address at all. Lossless and lossy compression, embedded lossy to lossless coding, progressive transmission by pixel accuracy and by resolution, robustness to the presence of bit-errors and region-of-interest coding, are some representative features. It is interesting to note that JPEG2000 is being designed to address the requirements of a diversity of applications, e.g. Internet, color facsimile, printing, scanning, digital photography, remote sensing, mobile applications, medical imagery, digital library and E-commerce.

The JPEG2000 still image coding system: an overview

JPEG2000: the upcoming still image compression standard

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.

The JPEG 2000 Suite

A new fast pruning algorithm is proposed for computing the N/sub 0/ lowest frequency components of a length-N discrete cosine transform, where N/sub 0/ is any integer less than or equal to N, and N=2/sup m/. The computational complexity of the developed algorithm is lower than any of the existing algorithms, resulting in significant time savings. In the special case that N/sub 0/=2/sup m0/, the required number of multiplications and additions is 1/2 m/sub 0/N and (m/sub 0/+1)N+( 1/2 m/sub 0/-2)N/sub 0/+1, respectively. >

Athanassios N. Skodras

Papers

The JPEG 2000 still image compression standard

The JPEG2000 still image coding system: an overview

JPEG2000: the upcoming still image compression standard

The JPEG 2000 Suite

Fast discrete cosine transform pruning