About the Author.Foreword.Preface.Glossary.1. Introduction.2. Video Formats and Quality.3. Video Coding Concepts.4. The MPEG-4 and H.264 Standards.5. MPEG-4 Visual.6. H.264/MPEG-4 Part 10.7. Design and Performance.8. Applications and Directions.Bibliography.Index.

H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia

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Principles Of Optics Electromagnetic Theory Of Propagation Interference And Diffraction Of Light

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

Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in . This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.

/pdf/sparse-representation-for-color-image-restoration-zgu54hue6s.pdf

Sparse Representation for Color Image Restoration

Quantization is a process that maps a continous or discrete set of values into approximations that belong to a smaller set. Quantization is a lossy: some information about the original data is lost in the process. The key to a successful quantization is therefore the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.

Quantization

This paper presents an adaptive demosaicing algorithm. Missing green samples are first estimated based on the variances of the color differences along different edge directions. The missing red and blue components are then estimated based on the interpolated green plane. This algorithm can effectively preserve the details in texture regions and, at the same time, it can significantly reduce the color artifacts. As compared with the latest demosaicing algorithms, the proposed algorithm produces the best average demosaicing performance both objectively and subjectively

/pdf/color-demosaicing-using-variance-of-color-differences-ehgjony42a.pdf

Color Demosaicing Using Variance of Color Differences

A unified approach to the realization of forward and inverse discrete cosine transforms is proposed. With this approach, an odd prime length DCT/IDCT with two half-length convolutions can be realized without extra overhead in terms of the number of multiplications. The formulation is most suitable for realization using distributed arithmetic, in which case typical convolvers can be used as the core unit for the hardware implementation of the transforms. Hence, an efficient unified DCT/IDCT chip is proposed to demonstrate the superiority of the formulation. The proposed architecture can easily meet the speed requirement of 14.3 MHz real-time operation with the current 2 mu m CMOS technology. >

/pdf/on-the-realization-of-discrete-cosine-transform-using-the-6rpbsmqm1c.pdf

On the realization of discrete cosine transform using the distributed arithmetic

In most digital cameras, Bayer color filter array (CFA) images are captured and demosaicing is generally carried out before compression. Recently, it was found that compression-first schemes outperform the conventional demosaicing-first schemes in terms of output image quality. An efficient prediction-based lossless compression scheme for Bayer CFA images is proposed in this paper. It exploits a context matching technique to rank the neighboring pixels when predicting a pixel, an adaptive color difference estimation scheme to remove the color spectral redundancy when handling red and blue samples, and an adaptive codeword generation technique to adjust the divisor of Rice code for encoding the prediction residues. Simulation results show that the proposed compression scheme can achieve a better compression performance than conventional lossless CFA image coding schemes.

/pdf/a-lossless-compression-scheme-for-bayer-color-filter-array-1w6xeexmro.pdf

A Lossless Compression Scheme for Bayer Color Filter Array Images

This letter presents a new approach to reduce coding artifacts in transform image coding We approach the problem in an estimation of each transform coefficient from its quantized version with its local mean and variance The proposed method can significantly reduce coding artifacts of low bit-rate coded images, and at the same time guarantee that the resulting images satisfies the quantization error constraint

/pdf/reduction-of-block-transform-image-coding-artifacts-by-using-5bjz27lazh.pdf

Reduction of block-transform image coding artifacts by using local statistics of transform coefficients

Multiscale error diffusion is superior to conventional error diffusion methods in digital halftoning as it can eliminate directional hysteresis completely. However, there is a bias to favor a particular type of dots in the course of the halftoning process. A new multiscale error diffusion method is proposed to improve the diffusion perfor- mance by reducing the aforementioned bias. The proposed method can eliminate the pattern noise in flat regions and the boundary effect found in some other conventional multiscale error diffusion methods. At the same time, it can preserve the local features of the input image in the output. This is critical to quality, especially when the resolution of the output is limited by the physical constraints of the display unit. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1758728)

/pdf/feature-preserving-multiscale-error-diffusion-for-digital-2jsdn91g99.pdf

Yuk-Hee Chan

Papers

Color Demosaicing Using Variance of Color Differences

On the realization of discrete cosine transform using the distributed arithmetic

A Lossless Compression Scheme for Bayer Color Filter Array Images

Reduction of block-transform image coding artifacts by using local statistics of transform coefficients

Feature-preserving multiscale error diffusion for digital halftoning