A Convolutional Neural Network Based Local Dimming Technology

doi:10.12068/j.issn.1005-3026.2021.05.003

Abstract

Abstract: Due to the light spreading problem， pixel compensation algorithms are difficult to accurately compensate brightness according to the extracted backlight information. Besides， a single compensation curve is difficult to adapt to the complex image content， resulting in unsatisfactory image quality. In order to improve the adaptability of pixel compensation， the idea of encoding and decoding in neural network was introduced. The deep feature of image was extracted by encoding network， and it was decoded by using the information of shallow feature in decoding network. A kind of classification-regression compensation neural network (CRCNN) was proposed. The experimental results show that the pixel compensation image obtained by this network can not only improve the subjective quality of the image， but also achieve good results in contrast， peak signal-to-noise ratio and other objective indicators.

Key words: local dimming; backlight extraction; pixel compensation; convolutional neural network; liquid crystal display

CLC Number:

TN27
TN873

ZHANG Tao， LIU Tian-wei， DU Wen-li. A Convolutional Neural Network Based Local Dimming Technology[J]. Journal of Northeastern University(Natural Science), 2021, 42(5): 624-632.

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