用于JPEG2000图像认证的鲁棒性水印算法

针对JPEG2000图像压缩标准所具有的渐进传输、一次编码多次解码等特性,提出了一种基于图像特征的鲁棒性图像认证算法.该算法在JPEG2000编码过程中,先根据图像不变特征,生成认证水印,再根据实际的鲁棒性认证需求,在量化后的小波系数中确定每个子带的认证水印嵌入位平面,最后基于小波系数位平面的特征嵌入认证水印.算法不仅能适应JPEG2000各种灵活的编码方式,还能定位图像篡改的位置.实验结果验证了图像认证算法对可允许图像操作的鲁棒性以及对图像篡改的敏感性.     

A new theoretical derivation of NFFT and its implementation on GPU

An efficient calculation of NFFT (nonequispaced fast Fourier transforms) is always a challenging task in a variety of application areas, from medical imaging to radio astronomy to chemical simulation. In this article, a new theoretical derivation is proposed for NFFT based on gridding algorithm and new strategies are proposed for the implementation of both forward NFFT and its inverse on both CPU and GPU. The GPU-based version, namely CUNFFT, adopts CUDA (Compute Unified Device Architecture) technology, which supports a fine-grained parallel computing scheme. The approximation errors introduced in the algorithm are discussed with respect to different window functions. Finally, benchmark calculations are executed to illustrate the accuracy and performance of NFFT and CUNFFT. The results show that CUNFFT is not only with high accuracy, but also substantially faster than conventional NFFT on CPU.     

Enhancement of membrane computing model implementation on GPU by introducing matrix representation for balancing occupancy and reducing inter-block communications

In previous studies, objects of each membrane were assigned to threads of one thread block of the graphic processing unit (GPU). The number of active threads was low if the number of objects inside a membrane was low. This study represents objects of membranes as entities of a matrix. Then a sub-matrix represents the appropriate number of objects assigned to threads of each thread block to balance the load and keep the occupancy high even when the number of objects per membrane is low. The size of the sub-matrix or the appropriate number of active threads is determined automatically. Furthermore, by this approach it is possible to assign more than one membrane to each thread block and to execute communication between membranes in the same thread block without the need for time-consuming inter-block communication. For example, using the previous algorithm, for two objects per membrane the speed up is 0.6×, while for the proposed algorithm the speed up is 32.4×.