Directional Discrete Cosine Transform Research Articles

Face recognition against illuminations using two directional multi-level threshold-LBP and DCT

In this paper, a new approach named as the Two Directional Multi-level Threshold-LBP Fusion (2D---MTLBP-F) is proposed to solve the problem of face recognition against illuminations. The proposed approach utilizes the Threshold Local Binary Pattern (TLBP) in combination with Discrete Cosine Transform (DCT). The utilization of LBP with different thresholds can produce different levels of information, which in turn can be used to improve performance for face recognition against illuminations. First, all images are normalised using a DCT normalisation technique in order to reduce negative effects of noise, blur or illumination. Secondly, the normalised images are transformed into 61 levels of TLBP with thresholds from ?30 to 30 and then the normalised DCT image is fused into these TLBP layers as it contains a different type of information in frequency domain. Thirdly, in the training stage, the 2D---MTLBP-F model is trained by searching for the best combination among these 62 layers (61 TLBP +1 DCT image) based on an idea from two dimensional multiple color fusion (2D---MCF). Fourthly, in testing stage for face recognition, all testing and gallery images are transformed into the 2D---MTLBP-F model, and face recognition is performed using the sparse sensing classifier (SRC). Finally, extensive experimental results on five different databases show that the proposed approach has achieved the highest recognition rates in different lighting conditions as well as in uncontrolled environment for FRGC database. In comparison with TLBP and the recently proposed approach of Multi-Scale Logarithm Difference Edge-maps (MSLDE), the proposed approach also achieves much better results on all used datasets.

Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures. Keywords: Ripplet, Directional discrete cosine transform (DDCT), Peak signal to noise ratio, MSE (mean square error), SC (structural content), MD (maximum difference), NCC (normalized cross correlation

Medical Image Registration Based on Feed-forward Neural Networks and Directional Discrete Cosine Transform

Registration is used to align two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. All large systems which evaluate images require the registration of images, which considered as an intermediate step. Examples of systems where image registration is a significant component include change detection using multiple images acquired at different times, fusion of image data from multiple sensor types, environmental monitoring, image mosaicing, weather forecasting, creating super resolution images, integrating information into Geographic Information Systems (GIS) and medical image analysis. In this research, the concentration is on registration of medical images. Analysis of multi-temporal medical images requires proper geometric alignment of the image to compare corresponding regions in each image volume. The proposed image registration technique consists of two steps. In the first step the feature extraction method is performed by using Directional Discrete Cosine Transform (DDCT). Finally the feed forward neural networks based image registration technique is performed .Comparison study in registration process between Directional Discrete Cosine Transform (DDCT) and Traditional Discrete Cosine Transform (DCT) is performed. Experiments have shown that the proposed method provides more accurate results for registration process when using DDCT than traditional DCT.

Color Image Compression Based on Directional All Phase Biorthogonal Transform

There are images in which some directional edges other than the vertical or horizontal ones dominate. So the conventional discrete cosine transform (DCT) may not be the best choice for them. Directional DCT framework is able to provide a better coding performance for image blocks that contain directional edges. In addition, the all phase biorthogonal transform (APBT) algorithm can improve the performance of compression. In the light of directional DCT and APBT, directional APBT is proposed and applied to color image coding. Experimental results show that this framework can indeed improve the compression performance of color image remarkably.

Multiple Description Coding Using Directional Discrete Cosine Transform

Delivery of high quality video over a wide area network with large number of users poses great challenges for the video communication system. To ensure video quality, multiple descriptions have recently attracted various attention as a way of encoding and visual information delivery over wireless network. We propose a new efficient multiple description coding (MDC) technique. Quincunx lattice sub-sampling is used for generating multiple descriptions of an image. In this paper, we propose the application of a directional discrete cosine transform (DCT) to a sub-sampled quincunx lattice to create an MDC representation. On the decoder side, the image is decoded from the received side information. If all the descriptions arrive successfully, the image is reconstructed by combining the descriptions. However, if only one side description is received, decoding is executed using an interpolation process. The experimental results show that such the directional DCT can achieve a better coding gain as well as energy packing efficiency than the conventional DCT with re-alignment.

Hybrid DDCT-PCA based multi sensor image fusion

Multi sensor image fusion algorithm based on directional Discrete Cosine Transform (DDCT) - Principal Component Analysis (PCA) hybrid technique has been developed and evaluated. The input images were divided into non-overlapping square blocks and the fusion process was carried out on the corresponding blocks. The algorithm works in two stages. In first stage, modes 0 to 8 were performed on images to be fused. For each mode, the coefficients from the images to be fused are used in the fusion process. The same procedure is repeated for other modes. Three different fusion rules are used in fusion process viz., 1. Averaging the corresponding coefficients (DDCTav), 2. Choosing the corresponding frequency band with maximum energy (DDCTek) and 3. Choosing the corresponding coefficient with maximum absolute value (DDCTmx) between the images. After this stage, there are eight fused images, one from each mode. In second stage, these eight fused images are fused using PCA. Performance of these algorithms were compared using fusion quality evaluation metrics such as root mean square error (RMSE), quality index (QI), spatial frequency and fusion quality index (FQI). It was concluded from the results that DDCTav performs poor and DDCTek performs slightly better than DDCTmx. Moreover, DDCTek is computationally simple and easily implementable on target hardware. Matlab code has been provided for better understanding.

Fast Directional Discrete Cosine Transforms Based Image Compression

PDF HTML阅读 XML下载 导出引用 引用提醒 图像压缩中的快速方向离散余弦变换 DOI: 10.3724/SP.J.1001.2011.03805 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(61072118); 国防科学技术大学预研基金 Fast Directional Discrete Cosine Transforms Based Image Compression Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:传统的二维DCT(discrete cosine transform)无法稀疏表示除水平或垂直方向以外的边缘,而具有强方向表示能力的方向预测离散余弦变换(directional prediction DCT,简称DPDCT)计算复杂度又过高.针对这些问题,提出了一种快速方向离散余弦变换(fast directional discrete cosine transform,简称FDDCT).该算法沿给定的方向模式进行变换,避免了DPDCT 中的插值运算,可以快速、稀疏地表示图像中各向异性边缘信息.此外,FDDCT 通过设计块边界提升,在进一步集中边缘能量的同时保证了算法的完全重构.实验结果表明,FDDCT 计算复杂度不超过DCT 的1.4 倍;采用同样的编码方法,基于FDDCT 的压缩图像与基于DCT 以及DPDCT 的压缩图像相比,峰值信噪比可提高0.4dB ~1.6dB,而且边缘细节更加清晰、完整. Abstract:The traditional discrete cosine transform (DCT) can only sparsely represent the horizontal and vertical edges in images. The computation complexity of directional prediction DCT (DPDCT), which has the ability to represent more directions, is much higher. To overcome these shortcomings, the fast directional discrete cosine transforms (FDDCT) is proposed in this paper, in which the transformation is performed on the predefined direction mode. Compared with DPDCT, no interpolation is needed in FDDCT, so FDDCT can sparsely represent the anisotropic edges in much faster images. A special lifting algorithm is designed between adjacent blocks to ensure the perfect reconstruction, which compacts energy in edges lying across the blocks. The experimental results show that the computation of FDDCT is no more than 1.4 times that of DCT’s. Coding with the same set partition method, PSNR compressed images that are combined with FDDCT are 0.4~1.6dB higher than those with DCT and DPDCT. Also, the edges and the details in the images are much clearer and less distortion exists. 参考文献 相似文献 引证文献

A NOVEL COMPRESSED IMAGES QUALITY METRIC

As the performance indicator of the image processing algorithms or systems, image quality assessment (IQA) has attracted the attention of many researchers. Aiming to the widely used compression standards, JPEG and JPEG2000, we propose a new no reference (NR) metric for compressed images to do IQA. This metric exploits the causes of distortion by JPEG and JPEG2000, employs the directional discrete cosine transform (DDCT) to obtain the detail and directional information of the images and incorporates with the visual perception to obtain the image quality index. Experimental results show that the proposed metric not only has outstanding performance on JPEG and JPEG2000 images, but also applicable to other types of artifacts.

Fast directional discrete cosine transform for image compression

A fast directional discrete cosine transform (FDDCT) is proposed for efficient representation of anisotropic edges in images. The transform is performed on the predefined direction lines similar to the intraprediction mode in H.264. Comparing to the directional discrete cosine transform (DDCT) now available, no interpolation is needed in FDDCT; thus, the amount of computation decreases by 80%. Simulation results indicate that the peak signal-to-noise ratios of images compressed using FDDCT are >1 db higher than those using DDCT.

H.264-based Wireless Surveillance Sensors in Application to Target Identification and Tracking

In this paper, the author propose a hardware-based m icro structure inside the architecture of H.264 encoder, called Vector Bank (VB), which can dram atically reduce the bandwidth, mem ory, and com putation tim e of a multi-cam era video surveillance system . VB is a m em ory-based structure that contains the vector inform ation for every single Macro Block (MB) of the video fram e from the Motion Estim ation m odule of the H.264 encoder. After extraction of the several vectors from several fram es, a dedicated Digital Signal Processor (DSP) can be assigned to analyze and predict the trajectories of the objects in m otion. With the application of 2-D edge detectors, such as Sobel or Laplacian of Gaussian (LoG) operators, the objects in m otion can be isolated from the background and thus be easily identified. In addition, a m iddleware-based technique derived from Directional Discrete Cosine Transform (DDCT) is introduced to im prove the im age quality without sacrificing the system perform ance. We propose four new m odes of operations in DDCT for better im age com pression ratio. The experim ental results show that we can im prove the im age quality of the targets, isolate them from the background, and track them easily in both linear and exponential trajectories of the m otion.

Directional Discrete Cosine Transforms—A New Framework for Image Coding

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.