Two interpolative BTC image coding schemes

Two interpolative BTC (block truncation coding) image coding schemes with optimal stack filters (in the minimum MAE sense) as the interpolator are suggested. Compared to the standard BTC, the new schemes yield >25% and >37.5% reductions in bit rate, respectively, with little performance degradation. Compared to a previous interpolative BTC coding method, the quality of coded images has been greatly improved with the new schemes (at the same or even a lower rate).<>     

Interpolative BTC image coding with vector quantization

The authors suggest two interpolative block truncation coding (BTC) image coding schemes with vector quantization and median filters as the interpolator. The first scheme is based on quincunx subsampling and the second one on every-other-row-and-every-other-column subsampling. It is shown that the schemes yield a significant reduction in bit rate at only a small performance degradation and, in general, better channel error resisting capabilities, as compared to the absolute moment BTC. The methods are further demonstrated to outperform the corresponding BTC schemes with pure vector quantization at the same bit rate and require minimal computations for the interpolation     

Color image fidelity metrics evaluated using image distortion maps

Several color image fidelity metrics are evaluated by comparing the metric predictions to empirical measurements. Subjects examined image pairs consisting of an original and a reproduction. They marked locations on the reproduction that differed detectably from the original. We refer to the distribution of error marks by the subjects as image distortion maps. The empirically obtained image distortion maps are compared to the predicited visible difference calculated using (1) the widely used root mean square error (point-by-point RMS) computed in uncalibrated RGB values, (2) the point-by-point CIELAB ΔE₉₄ values (CIE, 1994), and (3) S-CIELAB ΔE₉₄, a spatial extension of CIELAB ΔE metric. The uncalibrated RMS metric did not predict the perceptual image distortion data well. The point-by-point CIELAB ΔE₉₄ metric provided better predictions, and the S-CIELAB metric, which incorporated the spatial color sensitivity of the eye, gave the most accurate predictions. None of the metrics provided an excellent fit to the data. Image areas with poor predictions were concentrated in regions containing large negative local contrast. When these areas were excluded from our data analysis, both S-CIELAB and CIELAB predictions had much better agreement with the perceptual data. This suggests that the next step in improving color image fidelity metrics is to redefine color difference formula such as CIELAB ΔE₉₄ in terms of local contrast.     

Color image coding using morphological pyramid decomposition

Presents a new algorithm that utilizes mathematical morphology for pyramidal coding of color images. The authors obtain lossy color image compression by using block truncation coding at the pyramid levels to attain reduced data rates. The pyramid approach is attractive due to low computational complexity, simple parallel implementation, and the ability to produce acceptable color images at moderate data rates. In many applications, the progressive transmission capability of the algorithm is very useful. The authors show experimental results for color images at data rates of 1.89 bits/pixel.     

Color image processing using adaptive multichannel filters

New adaptive filters for color image processing are introduced and analyzed. The proposed adaptive methodology constitutes a unifying and powerful framework for multichannel signal processing. Using the proposed methodology, color image filtering problems are treated from a global viewpoint that readily yields and unifies previous, seemingly unrelated, results. The new filters utilize Bayesian techniques and nonparametric methodologies to adapt to local data in the color image. The principles behind the new filters are explained in detail. Simulation studies indicate that the new filters are computationally attractive and have excellent performance.     

综合BTC颜色矩和灰度共生矩阵的图像检索算法

针对图像检索的多特征融合问题,提出了一种综合BTC颜色矩和灰度共生矩阵的图像检索算法。该算法借鉴BTC的思想,对传统的颜色矩进行改进,将图像分为若干子块,每个子块按照阈值分割为两类像素,计算颜色矩,提取图像的颜色特征;利用灰度共生矩阵提取图像的纹理特征;通过颜色、纹理单特征相似度加权求和来表示图像的综合特征相似度,进行图像检索。实验结果表明,该算法能够综合颜色特征和纹理特征,有效地提高图像检索的查准率。     

A new algorithm for BTC image bit plane coding

A new look-up table technique for the coding of the block truncated image bit plane is presented which yields better images in the subjective as well as in the mean square sense compared to the one proposed by Mitchell and Delp (1980)     

Color image processing by using binary quaternion-moment-preservingthresholding technique

This paper presents a new moment-preserving thresholding technique, called the binary quaternion-moment-preserving (BQMP) thresholding, for color image data. Based on representing color data by the quaternions, the statistical parameters of color data can be expressed through the definition of quaternion moments. Analytical formulas of the BQMP thresholding can thus be determined by using the algebra of the quaternions. The computation time for the BQMP thresholding is of order of the data size. By using the BQMP thresholding, quaternion-moment-based operators are designed for the application of color image processing, such as color image compression, multiclass clustering of color data, and subpixel color edge detection. The experimental results show that the proposed operator for color image compression can have output picture quality acceptable to human eyes. In addition, the proposed edge operator can detect the color edge at the subpixel level. Therefore, the proposed BQMP thresholding can be used as a tool for color image processing.     

基于离散四元数余弦变换的彩色图像数字水印技术

将离散四元数余弦变换(DQCT)应用于彩色图像的数字水印技术中,提出了一种在彩色图像中嵌入水印的新技术。应用四元数理论把彩色图像作为一整体进行余弦变换,在彩色宿主图像的DQCT域中实现彩色水印图像的嵌入。实验表明,该方法具有较好的抗压缩能力,对高斯噪声、椒盐噪声和缩放攻击具有较好的鲁棒性。     

Effective palette indexing for image compression using self-organization of Kohonen feature map.

The process of limited-color image compression usually involves color quantization followed by palette re-indexing. Palette re-indexing could improve the compression of color-indexed images, but it is still complicated and consumes extra time. Making use of the topology-preserving property of self-organizing Kohonen feature map, we can generate a fairly good color index table to achieve both high image quality and high compression, without re-indexing. Promising experiment results will be presented.     

Computation-aware fast motion estimation for H.264/AVC using image indexing

The key to designing a real-time video coding system is efficient motion estimation, which reduces temporal redundancies. The motion estimation of the H.264/AVC coding standard can use multiple references and multiple block sizes to improve rate-distortion performance. The computational complexity of H.264 is linearly dependent on the number of allowed reference frames and block sizes using a full exhaustive search. Many fast block-matching algorithms reduce the computational complexity of motion estimation by carefully designing search patterns with different shapes or sizes, which have a significant impact on the search speed and distortion performance. However, the search speed and the distortion performance often conflict with each other in these methods, and their high computational complexity incurs a large amount of memory access. This paper presents a novel block-matching scheme with image indexing, which sets a proper priority list of search points, to encode a H.264 video sequence. This study also proposes a computation-aware motion estimation method for the H.264/AVC. Experimental results show that the proposed method achieves good performance and offers a new way to design a cost-effective real-time video coding system.     

Content based image indexing and retrieval using directional local extrema and magnitude patterns

In this paper, we integrate the concept of directional local extremas and their magnitude based patterns for content based image indexing and retrieval. The standard ditectional local extrama pattern (DLEP) extracts the directional edge information based on local extrema in 0°, 45°, 90°, and 135° directions in an image. However, they are not considering the magnitudes of local extremas. The proposed method integrates these two concepts for better retrieval performance. The sign DLEP (SDLEP) operator is a generalized DLEP operator and magnitude DLEP (MDLEP) operator is calculated using magnitudes of local extremas. The performance of the proposed method is compared with DLEP, local binary patterns (LBPs), block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG) and local edge patterns for image retrieval (LEPINV) methods by conducting two experiments on benchmark databases, viz. Corel-5K and Corel-10K databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to other existing methods on respective databases.     

Color object indexing and retrieval in digital libraries

In our previous work, illumination invariant object recognition was achieved by normalizing the three color bands. We further employed the compressed histogram of the chromaticity to arrive at a valuable representation of an object which can facilitate high retrieval accuracy. The first shortcoming of this method lies in the usage of a uniform quantization scheme in obtaining the chromaticity, which is not in agreement with the perception of the human vision system. In this paper, we develop an approach using the CIE UCS transform to circumvent this problem. Second, instead of using uncompressed images to achieve the illumination invariant indexing and retrieval, we carry out our indexing process directly in the DCT domain by using several coefficients from each macro-block. Third, in light of the special properties of the normalized chromaticity histogram frames, the foundation of the ensuing low-pass filtering, an additional step is inserted to render this frame smoother thus resulting in a better data reduction. Fourth, in order to facilitate efficient retrieval during the data query phase, which is of utmost importance in digital libraries, the 36-dimensional model vectors as the indices of model images in digital libraries are clustered by use of vector quantization techniques. This clustering strategy reduces the searching space by an order of magnitude. Desirable results have been observed from our experiments using the proposed color-object-indexing/retrieval algorithm.     

Data hiding approach for efficient image indexing

A data hiding approach to embed the indexing keys inside the JPEG compressed images for their retrieval, inspired from the spirit of digital watermarking, is proposed. At the stage of database population, the existing approach is to compress all images by standard JPEG in order to save their storage space, but this ignores the compression of their indexing keys under the notion that the size of each indexing key is negligible. When a large image database is established, however, this part of storage space becomes non-trivial. By hiding the indexing keys inside the JPEG compressed codes, significant advantages are gained in that the indexing keys can be compressed naturally by JPEG without any additional cost. Experiments on hiding an indexing key of 256 bytes illustrate that: no noticeable distortion is introduced in comparison with non-watermarked images; and their storage space is reduced into the region of 7-27% of their original size.     

改进的综合BTC颜色矩和DT-CWT的图像检索算法

特征提取是基于内容的图像检索中的关键技术.针对基于单一特征检索效果不理想的问题,提出一种改进的综合颜色和纹理特征的图像检索算法.该算法在YIQ颜色空间中进行特征提取,首先结合方块编码(BTC)的思想,提取颜色矩作为颜色特征;采用双树复小波变换(DT-CWT)提取纹理特征,融合两种特征并利用相似性度量方式进行图像检索.实验结果表明算法所提取的颜色、纹理特征更利于检索,使用综合特征检索的平均查准率比同类算法更高.     

MRI and CT image indexing and retrieval using local mesh peak valley edge patterns

In this paper, a new pattern based feature, local mesh peak valley edge pattern (LMePVEP) is proposed for biomedical image indexing and retrieval. The standard LBP extracts the gray scale relationship between the center pixel and its surrounding neighbors in an image. Whereas the proposed method extracts the gray scale relationship among the neighbors for a given center pixel in an image. The relations among the neighbors are peak/valley edges which are obtained by performing the first-order derivative. The performance of the proposed method (LMePVEP) is tested by conducting two experiments on two benchmark biomedical databases. Further, it is mentioned that the databases used for experiments are OASIS−MRI database which is the magnetic resonance imaging (MRI) database and VIA/I–ELCAP-CT database which includes region of interest computer tomography (CT) images. The results after being investigated show a significant improvement in terms average retrieval precision (ARP) and average retrieval rate (ARR) as compared to LBP and LBP variant features.     

Frequency layered color indexing for content-based image retrieval

Image patches of different spatial frequencies are likely to have different perceptual significance as well as reflect different physical properties. Incorporating such concept is helpful to the development of more effective image retrieval techniques. We introduce a method which separates an image into layers, each of which retains only pixels in areas with similar spatial frequency characteristics and uses simple low-level features to index the layers individually. The scheme associates indexing features with perceptual and physical significance thus implicitly incorporating high level knowledge into low level features. We present a computationally efficient implementation of the method, which enhances the power and at the same time retains the simplicity and elegance of basic color indexing. Experimental results are presented to demonstrate the effectiveness of the method.     

System profiles in content-based image indexing and retrieval

In this paper, a novel study on system profiles and adaptation of parameters for end-users of content-based indexing and retrieval (CBIR) applications are presented. The main objective of the study is improving the overall CBIR application performance in different hardware platforms having different technical capabilities and conditions. We define CBIR system profiles in terms of hardware and system platform attributes and propose CBIR parameters for each profile. Hence, the study consists of two main parts: system profiling and adaptation of indexing and retrieval parameters for each profile. The proposed CBIR parameters are appropriate configurations for optimal CBIR use on every platform. The proposed parameters for each system profile are assessed over a large set of experiments. Experimental studies show that the proposed parameters for each system profile have satisfactory semantic retrieval performance, with reduced computational complexity and storage space requirement. 45 to 78% improvement is achieved in the computational complexity of the retrieval process depending on the profile.     

Recover the tampered image based on VQ indexing

In this paper, a tampered image recovery scheme is proposed by creating an index table of the original image via vector quantization (VQ) and embedding it into the original image as a basis for recovery. In order to complete the goal of image authentication and recovery, Wong's watermarking scheme [P.W. Wong, N. Memon, Secret an public key image watermarking schemes for image authentication and ownership verification, IEEE Trans. Image Process 10 (10) (2001) 1593–1601] is employed to perform tamper detection. Wong's watermarking scheme can be used to accurately locate tampered regions. If an image has been tampered, the index table can be used to recover the tampered regions. The experimental results indicate that our scheme has the higher probability of image recovery. Besides, compared with Lee and Lin's [Dual watermark for image tamper detection and recovery, Pattern Recognition 41 (2008) 3497–3506] scheme, our scheme provides not only a better quality of recovered images but also better results at edge regions.     

一种改进的综合BTC颜色矩和DT-CWT的图像检索算法

摘 要：特征提取是基于内容的图像检索中的关键技术。针对基于单一特征检索效果不理想的问题,提出一种改进的综合颜色和纹理特征的图像检索算法。该算法在YIQ颜色空间中进行特征提取,首先结合方块编码（BTC）的思想,提取颜色矩作为颜色特征;采用双树复小波变换（DT-CWT）提取纹理特征,融合两种特征并利用相似性度量方式进行图像检索。实验结果表明算法所提取的颜色、纹理特征更利于检索,使用综合特征检索的平均查准率比同类算法更高。