This paper presents a power-constrained contrast enhancement algorithm for organic light-emitting diode display based on multiscale retinex (MSR). In general, MSR, which is the key component of the proposed algorithm, consists of power controllable log operation and subbandwise gain control. First, we decompose an input image to MSRs of different sub-bands, and compute a proper gain for each MSR. Second, we apply a coarse-to-fine power control mechanism, which recomputes the MSRs and gains. This step iterates until the target power saving is accurately accomplished. With video sequences, the contrast levels of adjacent images are determined consistently using temporal coherence in order to avoid flickering artifacts. Finally, we present several optimization skills for real-time processing. Experimental results show that the proposed algorithm provides better visual quality than previous methods, and a consistent power-saving ratio without flickering artifacts, even for video sequences.

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This study proposes a subpixel-based image downsampling algorithm using content-adaptive two-dimensional (2D) finite impulse response (FIR) filters. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, using a sufficient number of low-resolution (LR) and high-resolution (HR) patch pairs, the authors compute optimal 2D FIR filters to synthesise LR patches of the highest quality from a specific HR patch and store the patch-adaptive 2D FIR filters in a dictionary. In the inference stage, they explore candidates that best match to each HR input patch in the dictionary and synthesise LR patches by using their corresponding 2D FIR filters on a subpixel basis. The experimental results show that the proposed algorithm produces higher-quality LR images on a patch basis than existing methods and entails no blur and aliasing artefacts.