Image-classification-based global dimming algorithm for LED backlights in LCDs

Feng, Qianjin; Huijie, He; Dong, Han; Zhang, Lei; Lv, Guoqiang

doi:10.1016/j.optlastec.2014.12.021

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…Until now, LCD still acts as the dominant display technology in high-reliability applications, such as digital signage displays, TV, and cockpit displays, among others. In order to improve the HDR and uniformity of brightness at low grayscale of LCD [8], dimmable backlight has been proposed, including global dimming [9], [10], [11] and local dimming [12], [13], [14], [15], [16], [17], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Local Dimming Algorithms on the Halo Effect in LCD With Local Dimming Mini-LED Backlight

Zhou,

Gao,

Ruan

et al. 2024

IEEE Photonics J.

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The local dimming mini-LED backlight has become one of the most important technologies for liquid crystal display (LCD), facilitating the improvement on high dynamic range (HDR) and power saving. The halo effect still limits the performance of LCDs employing mini-LED backlight. The impact of the number of local dimming zones on the contrast ratio has been investigated. The halo effect has also been investigated under four typical local dimming algorithms and different local dimming zones. It has been experimentally demonstrated that the halo area gradually shrinks with increasing the number of local dimming zones, contributing to the improvement on the HDR of LCD.

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Section: Introductionmentioning

confidence: 99%

Impacts of Local Dimming Algorithms on the Halo Effect in LCD With Local Dimming Mini-LED Backlight

Zhou,

Gao,

Ruan

et al. 2024

IEEE Photonics J.

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“…With the disadvantage of low contrast ratio (CR), LCDs have been greatly challenged by OLED displays. There exist two techniques to improve CR, including dynamic dimming [1][2] and dual-panel display [3][4]. Dual-panel technique can increase CR by decreasing light leakage via bonding two pieces of LC panels by optically clear adhesive (OCA).…”

Section: Introductionmentioning

confidence: 99%

P‐101: Convolutional Neural Network to Optimize Dual‐Panel Display

Feng,

Zhang,

Ren

et al. 2023

Symp Digest of Tech Papers

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Liquid crystal displays (LCDs) have the disadvantage of light leakage, resulting in low contrast ratio (CR). Dual‐layer LCDs can greatly improve CR by bonding two layers of LC panels. However, there is a certain distance between the two LC panels, and the ghost may appear when viewing off‐axis. At present, the solutions for this problem cannot achieve a good balance on processing time and display quality. In this paper, convolutional neural network (CNN) is used to optimize dual‐layer display as well as control processing time. The residual blocks are included in the network to improve the quality of the output image. The two output images that correspond to two layers of LC panels are reconstructed from different viewing angles to make comparisons with other methods in terms of processing time and display quality. The simulation results show that compared with other methods, the proposed CNN method can greatly reduce the processing time while maintaining high display quality, which presents high practicability.

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“…With such a disadvantage, LCDs have been greatly challenged by OLED displays. There exist two techniques to improve CR, including dynamic dimming [1][2] and dual-layer display [3][4]. Dual-layer technique can greatly reduce light leakage via bonding two pieces of LC panels.…”

Section: Introductionmentioning

confidence: 99%

P‐8.2: CNN for Dual‐layer LCDs

Zhang,

Ren,

Feng

et al. 2023

Symp Digest of Tech Papers

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Low contrast ratio (CR) is one big drawback for LCD, which is caused by light leakage of LC cell. Light leakage can be greatly reduced by bonding two LC panels to form a dual‐layer LCD. However, with the certain distance between the two LC panels, the ghost may appear when viewing off‐axis. This paper proposes a convolutional neural network‐based method to improve the display quality of a dual‐layer LCD. The network consists of 8 convolution layers, in which the kernel of the layer 4 and layer 8 is 1×1 to eliminate the redundant features and accelerate the processing speed. The kernel of the other layers is 3×3. Concat1 and Concat2 are included to fuse the features from the convolution layer 1‐3 and 5‐7. The loss function is especially defined with the consideration of the total differences between the reconstructed images at the different viewing angles and the input image. The simulation results show that compared with the existing methods, the proposed method has the merits of higher display quality and shorter processing time, which presents high practicability.

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Image-classification-based global dimming algorithm for LED backlights in LCDs

Cited by 9 publications

References 17 publications

Impacts of Local Dimming Algorithms on the Halo Effect in LCD With Local Dimming Mini-LED Backlight

Impacts of Local Dimming Algorithms on the Halo Effect in LCD With Local Dimming Mini-LED Backlight

P‐101: Convolutional Neural Network to Optimize Dual‐Panel Display

P‐8.2: CNN for Dual‐layer LCDs

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