In this paper, a cost-effective organic light-emitting diode (OLED) display burn-in compensation method on a mobile system is proposed. The subpixel wise compensation map is estimated using the data-counting approach and is reallocated to multiunit regional map according to amount of burn-in details.The degraded luminance compensation is applied for the display driver integrated circuit (IC) without the additional external storage. With the optimized multiunit map, perceptual quality can be enhanced after compensation with limited internal memory. Experiment shows that the luminance uniformity of the degraded panel has been improved by 20% after proposed compensation with 1/16 memory space usage.
A data-driven image reproduction algorithm on the conventional simple image pipeline with deep neural networks is proposed. The deep neural networks determining the pipeline parameter values are learned by end-to-end training of the pipeline. The algorithm is efficient because the network operates in low-resolution mode and the image operators for the pixel-wise image full-resolution transfers are ready in most of display image pipeline hardware. A user study on the challenging images indicates that the model performs well on complicated content-dependent image enhancement transformation: the image appearance is preferred to several other methods.
In this paper, a cost‐effective OLED display burn‐in compensation method on a mobile system is proposed. The sub‐pixel wise compensation map is estimated using the data‐counting approach and is reallocated to multi‐unit regional map according to amount of burn‐in details. The degraded luminance compensation is applied for the display driver IC without the additional external storage. With the optimized multi‐unit map, perceptual quality can be enhanced after compensation with limited internal memory. Experiment shows that the luminance uniformity of the degraded panel has been improved by 20% after proposed compensation with 1/16 memory space usage.
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