...
首页> 外文期刊>Broadcasting, IEEE Transactions on >No-Reference PSNR Estimation for HEVC Encoded Video
【24h】

No-Reference PSNR Estimation for HEVC Encoded Video

机译:HEVC编码视频的无参考PSNR估计

获取原文
获取原文并翻译 | 示例
           

摘要

Video quality estimation is considered a means of monitoring quality of service in broadcasting or IPTV services. In this paper, a no-reference peak signal-to-noise ratio (PSNR) estimation method is first presented for a quadtree-based motion estimation or compensation and transform coding scheme such as HEVC test model (HM), which is expected to be popularly used due to its highly enhanced coding efficiency, in 2-D and 3-D high resolution videos. The proposed no-reference PSNR estimation method is based on a Laplacian mixture distribution, which takes into account the distribution characteristics of residual transform coefficients in different quadtree depths and coding types of coding units (CUs). In order to predict the model parameters of the Laplacian mixture distribution for all zero quantized coefficients case, an exponential regression scheme is employed over quadtree depth levels of CUs. The proposed no-reference PSNR estimation method yields fairly accurate results from 0.970 to 0.983 in correlation and from 0.530 to 0.890 in RMSE between the actual and the estimated PSNR values for HM encoded bitstreams, outperforming single PDF based models.
机译:视频质量估计被认为是监视广播或IPTV服务中服务质量的一种手段。本文首先针对基于四叉树的运动估计或补偿和变换编码方案(例如HEVC测试模型(HM)),提出了一种无参考峰值信噪比(PSNR)估计方法。由于其在2D和3D高分辨率视频中的编码效率大大提高而得到广泛使用。所提出的无参考PSNR估计方法基于拉普拉斯混合分布,该模型考虑了不同四叉树深度中残差变换系数的分布特性以及编码单元(CU)的编码类型。为了预测所有零量化系数情况下拉普拉斯混合分布的模型参数,在CU的四叉树深度级别上采用了指数回归方案。所提出的无参考PSNR估计方法在HM编码比特流的实际PSNR值和估计PSNR值之间,从0.970到0.983的相关性和从RMSE的0.530到0.890的结果非常准确,胜过基于单个PDF的模型。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号