The thin film transistor liquid crystal display (TFT-LCD) image has nonuniform brightness, which is a major difficulty in finding the Mura defect region. To facilitate Mura segmentation, globally widely varying background signal must be flattened and then Mura signal must be enhanced. In this paper, Mura signal enhancement and background-signalflattening methods using wavelet coefficient processing are proposed. The wavelet approximation coefficients are used for background-signal flattening, while wavelet detail coefficients are employed to magnify the Mura signal on the basis of an adapted contrast sensitivity function (CSF). Then, for the enhanced image, trimodal thresholding segmentation technique and a false-region elimination method based on the human visual system (HVS) are employed for reliable Mura segmentation. The experimental results show that the proposed algorithms produce promising results and can be applied to automated inspection systems for finding Muras in a TFT-LCD image. key words: TFT-LCD, inspection, wavelet transform, human visual system, contrast sensitivity function
A new lip-synchronization (lip-sync) test method using audio and video signals for DTV is presented. The proposed method does not interfere with or have any effect on the program being broadcast, as the time-indexed lip-sync test signals (TILTS) are embedded in a hidden region. The video TILTS is embedded into transient effect areas outside the active pixel region, making it invisible on-screen. Experimental results confirm that the time difference between audio and video signals can be easily measured at any time from the TILTS in the decoder outputs. Index Terms-DTV, lip-sync, TATS (transient effect area test signal), TILTS (time indexed lip-sync test signal).
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