Abstract In this paper, a new backlight dimming method using weighted mean-square-error (MSE) based on joint edge-saliency characteristics is proposed. In contrast to conventional backlight dimming methods that cannot accurately evaluate the saturation error and determine the optimal clipping point to achieve the best trade-off between image quality and power consumption, the proposed method, with consideration of human visual characteristics, analyzes the joint edge-saliency characteristics based on spatio-temporal saliency map and edge-strength map of the input images, and weights the MSE accordingly. It can therefore evaluate the allowable saturation error and determine the optimal clipping point. Simulation results show that the proposed method outperforms other benchmark methods in the case of overall consideration of image quality and power consumption. Additionally, the proposed method maximizes the power reduction rate while successfully preserving image details in the regions of interest and maintaining high perceived image quality. Our results also show that the average computation time of the proposed algorithm was reduced by up to 30.184%, 19.162% and 8.027%, respectively, compared with I2GEC, SMVA2 and SPBD algorithms.

中文翻译：

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基于联合边缘显着特征的加权均方误差背光调光方法性能分析

摘要 本文提出了一种基于联合边缘显着特征的加权均方误差(MSE)背光调光方法。与传统背光调光方法无法准确评估饱和度误差并确定最佳剪裁点以实现图像质量和功耗之间的最佳权衡不同，所提出的方法结合人类视觉特征，分析了联合边缘-基于输入图像的时空显着性图和边缘强度图的显着性特征，并相应地对 MSE 进行加权。因此，它可以评估允许的饱和误差并确定最佳剪切点。仿真结果表明，该方法在综合考虑图像质量和功耗的情况下优于其他基准方法。此外，所提出的方法最大限度地降低了功率降低率，同时成功地保留了感兴趣区域中的图像细节并保持了高感知图像质量。我们的结果还表明，与 I2GEC、SMVA2 和 SPBD 算法相比，所提出算法的平均计算时间分别减少了 30.184%、19.162% 和 8.027%。