A new LCD driving scheme, Super Impulsive Technology (SIT), is proposed to improve moving image quality without sacrifice of wide viewing angle performance. This new method is most suitable for impulsive driving of the S‐PVA two transistor sub‐pixel structure. The algorithm is compatible with motion interpolated 120Hz sources as well as conventional 60Hz sources. SIT eliminates motion artifacts in moving images by combining fast driving with the impulsive effect.

Abstract— An impulsive driving technique has been widely adopted for the elimination of motion blur in LCDs. Although the problem of slow temporal response time is very well known for LCDs, the inherent motion blur of moving objects in hold‐type displays has a more‐serious impact on display performance. It is well known that even very fast LCDs with zero response time still suffer from the motion‐blur artifact due to hold‐type driving effects. However, a fast temporal response is also critical in order to maximize the blur‐reduction effect even in the case of impulsive driving. In this paper, the special behavior of LC molecules in an impulsive driving environment has been analyzed especially for the case of black‐frame insertion, and we propose an effective means to implement optimized response‐time compensation (RTC) for the black‐frame insertion technique.

Recently, a single phase driving method has been adopted for driving CCFL lamps to provide a low cost implementation. However, high voltage driving signals are coupled to pixel electrodes through parasitic capacitance between the lamps and panel electrodes, generating artifacts in the display. This paper describes the analysis results of this phenomenon and the proposed automatic delay compensation scheme eliminates the artifacts caused by the coupling effect.

This paper proposes an adaptive threshold overdriving algorithm to avoid exacerbating noise problems in LCD‐TVs. In conventional LCD‐TVs, video image noise is boosted by the timing controller's overdrive function, resulting in harsh noise as perceived by the human visual system. Various gray data and noise levels have been analyzed, and the conditions which generate the boosted noise have been extracted and applied to a new algorithm to adjust overdrive threshold criteria according to the input images. The new overdriving scheme shows greatly improved noise performance compared to conventional fixed threshold schemes.