Fast Control for Backlight Power-Saving Algorithm Using Motion Vectors from the Decoded Video Stream

Abstract

Backlight power-saving algorithms can reduce the power consumption of the display by adjusting the frame pixels with optimal clipping points under some tradeoff criteria. However, the computation for the selected clipping points can be complex. In this paper, a novel algorithm is created to reduce the computation time of the state-of-the-art backlight power-saving algorithms. If the current frame is similar to the previous frame, it is unnecessary to execute the backlight power-saving algorithm for the optimal clipping points, and the derived clipping point from the previous frame can be used for the current frame automatically. In this paper, the motion vector information was used as the measurement of the similarity between adjacent frames, where the generation of the motion vector information requires no extra complexity since it is generated to reconstruct the decoded frame pixels before the display. The experiments showed that the proposed work can reduce the running time of the state-of-the-art methods by 25.21% to 64.22%, while the performances are maintained; the differences with the state-of-the-art methods in PSNR are only 0.02~1.91 dB, and those in power are only −0.001~0.008 W.

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs

“…Therefore, a well-trained model from this project could significantly assist dentists in diagnosis. Dentists can use AI-classified images for pre-screening and comparison and then perform further detailed invasive examinations [9]. The goal of this research is to construct a CNN model [10] that can identify the presence or absence of FI [11] through transfer learning.…”