Abstract— In this paper, an active backlight control technology and a data‐processing algorithm has been developed to improve the image quality in IPS‐mode LCD TVs. The image‐blinking problem caused by repeatedly abrupt changes in the backlight luminance was solved by using algorithms [Fba (flexible‐boundary algorithm) and Cfa (cumulative feedback algorithm)] and an optimized number of backlight dimming steps based on human perception. In the IPS‐mode 42‐in. TFT‐LCD panel, the dynamic contrast ratio can be more than twice the typical level by means of a lower black luminance and a higher white luminance. Additionally, the power consumption and LCD temperature were lowered.
The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.
Nowadays, deep neural networks (DNNs) for image processing are becoming more complex; thus, reducing computational cost is increasingly important. This study highlights the construction of a DNN for real‐time image processing, training various image processing operators efficiently through multitask learning. For real‐time image processing, the proposed algorithm takes a joint upsampling approach through bilateral guided upsampling. For multitask learning, the overall network is based on an encoder‐decoder architecture, which consists of encoding, processing, and decoding components, in which the encoding and decoding components are shared by all the image processing operators. In the processing component, a semantic guidance map, which contains processing information for each image processing operator, is estimated using simple linear shifts of the shared deep features. Through these components, the proposed algorithm requires an increase of only 5% in the number of parameters to add another image processing operator and achieves faster and higher performance than that of deep‐learning‐based joint upsampling methods in local image processing as well as global image processing.
In active matrix organic light emitting diode (AMOLED) displays, when a variable refresh rate is applied and the frame rate changes, the image’s color and luminance quality in AMOLED displays deteriorates. The frequency-dependent cognitive differences were experimentally demonstrated by using 6.76″ AMOLED displays. This phenomenon is dependent on the emission time and the data programming time on the frame rate. This degradation of the image quality during the frequency switch could be prevented by applying a variable initial voltage (VINI) to the OLED anode. For a frequency change between 60 and 120 Hz, the measured just noticeable color difference (JNCD) decreased from 7.50 to less than 1.00 in luminance, and from 2.34 to 0.02 in color. As our approach can prevent image quality distortion by utilizing an existing compensation pixel structure without additional compensation steps, it will be a promising technique for improving the picture quality in AMOLED displays.
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