Video adaptation is a promising technique to bridge the gap between network status,device capabilities,and user preferences in pervasive media applications.However,conventional adaptation frameworks based on either transcoding or multiple pre-transcoding are not able to accommodate large numbers of users with diversified applications.This paper introduces an intermediate video description called "Intermedia",which consists of multiple level video signal components,such as texture,motion,and rate control information,as well as some semantic features,such as structural characteristics and Region Of Interest (ROI) information.It is generated off-line and stored in the video server or media gateway.Intermedia is then used to design a novel video adaptation system.The proposed adaptation system quickly and easily generates the required bit stream from Intermedia with very low complexity to fulfill a series of specific adaptation requirements,e.g.,bitrate conversion,temporal/spatial resolution reduction,video summarization,ROI browsing,and some multi-level adaptations involving both signal level and semantic level adaptation.The satisfactory performance of such a system demonstrates the effectiveness and efficiency of the proposed video adaptation framework.
NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of NLM.Nevertheless,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of noise.In this paper,an improved scheme for image denoising is proposed.This scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of noise.PCA is then used to project those filtered image neighborhood vectors onto a lower-dimensional space.With the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual fidelity.The experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.
High-speed high-resolution analog-to-digital(A/D) conversion demanded by ultra wideband(UWB) signal processing is a very challenging problem.This paper proposes a parallel random projection method for UWB signal acquisition.The proposed method can achieve high sampling rate,high resolution and technical feasibility of hardware implementation.In the proposed method,an analog UWB signal is projected over a set of random sign functions.Then the low-rate high-resolution analog-to-digital convertors(ADCs) are used to sample the projection coefficients.The signal can be reconstructed by simple linear calculation with the sampling matrix,without complying with optimization algorithm and prior knowledge.In other aspects,unlike other approaches that need to utilize an accurate time-shift at extremely high frequency,or design a hybrid filter bank,or generate specific basis functions or work for signals with prior knowledge,the proposed method is a universal sampling approach and easy to apply.The simulation results of signal to noise ratio(SNR) and spurious-free dynamic range(SFDR) validate the efficiency of the proposed method for UWB signal acquisition.
为提高地震数据压缩感知重构的信噪比和保真度,提出一种基于曲波变换的地震数据压缩感知重构算法。建立了地震数据压缩感知重构模型,分析了基于曲波变换稀疏表示的地震数据各尺度之间能量与熵的分布特性,结合分块压缩感知技术降低随机观测的计算复杂度,利用曲波变换稀疏表示高频区域各尺度之间的相关性,设计了随信息熵变化的自适应双变量收缩阈值迭代重构的方法。实验结果表明,在相同的采样率下,该算法重构的地震数据峰值信噪比提高了1.5 d B以上,并且具有良好的细节信息保持能力。
