排序方式: 共有6条查询结果,搜索用时 15 毫秒
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While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this pa-per, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D im-ages/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images. 相似文献
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基于ADV212的实时图像压缩系统 总被引:1,自引:1,他引:0
采用专用图像压缩芯片ADV212设计了一个能对分辨力高、数据量大的图像进行实时压缩的系统.该系统能够根据输入数据率自适应调整压缩比,实时产生JPEG2000格式的码流.ADV212输出的码流经过加密后可以实时输出也可在本系统内存储.实验结果表明,该系统能满足实时性要求,同时所得重建图像具有较好的主观视觉感受和较高的峰值信噪比. 相似文献
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In order to improve the adaptiveness of TV/L2-based image denoising algorithm in differ- ent signal-to-noise ratio (SNR) environments, an iterative denoising method with automatic parame- ter selection is proposed. Based upon the close connection between optimization function of denois- ing problem and regularization parameter, an updating model is built to select the regularized param- eter. Both the parameter and the objective function are dynamically updated in alternating minimiza- tion iterations, consequently, it can make the algorithm work in different SNR environments. Mean- while, a strategy for choosing the initial regularization parameter is presented. Considering Morozov discrepancy principle, a convex function with respect to the regularization parameter is modeled. Via the optimization method, it is easy and fast to find the convergence value of parameter, which is suitable for the iterative image denoising algorithm. Comparing with several state-of-the-art algo- rithms, many experiments confirm that the denoising algorithm with the proposed parameter selec- tion is highly effective to evaluate peak signal-to-noise ratio (PSNR) and structural similarity 相似文献
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