Optimal local dimming based on an improved greedy algorithm

被引:12
|
作者
Zhang, Tao [1 ,2 ]
Zeng, Qin [1 ,2 ]
Zhao, Xin [1 ,2 ]
机构
[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
[2] Tianjin Univ, Texas Instruments DSP Joint Lab, Tianjin 300072, Peoples R China
关键词
Local dimming; Optimization; Visual quality; IGRA; CONTRAST ENHANCEMENT; GENETIC ALGORITHM; BACKLIGHT; OPTIMIZATION; SYSTEM; POWER; ADAPTATION; DISPLAYS;
D O I
10.1007/s10489-020-01769-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As a new technology appeared in recent years, the local dimming can effectively reduce the power consumption of a display system and improve its display effect. A suitable local dimming algorithm should have efficient performance and can make the displayed images have higher visual quality. However, most of the existing local dimming methods can not have both of the above advantages. In this paper, the local dimming is taken as an optimization problem. On the basis of our previous work which focuses on reducing the image distortion and power consumption, image contrast ratio which is another important factor of visual quality is also considered. To improve the running efficiency of local dimming, the Greedy Algorithm (GRA) which is one of the simplest heuristic algorithms is used to design the local dimming algorithm. In order to improve the global optimization ability of the GRA, an Improved Greedy Algorithm(IGRA) based on the strategies of Taking out-Putting in and variable search step size is proposed. Experienced in four different types of images and compared with five parameter-based algorithms, the IGRA can obtain a higher visual quality under the same or lower power consumption. It is also proved that the IGRA has more powerful search ability and higher running efficiency by the comparisons with the Improved Shuffled Frog Leaping Algorithm (ISFLA) proposed in our previous work, and two recent algorithms including the Modified Genetic Algorithm (MGA) and the Improved Particle Swarm Optimization (IPSO).
引用
收藏
页码:4162 / 4175
页数:14
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