Super-resolution (SR) is a technique that reconstructs high-resolution images using the information present in low-resolution images. Due to their potentials of being used in wide range of image and video applications, various SR algorithms have been studied and proposed in the literature until recently. However, many of the algorithms provide insufficient perceptual quality, possess high computational complexity, or have high memory requirement, which make them hard to apply on consumer-level products. Therefore, in this paper we propose an effective super-resolution method that not only provides an excellent visual quality but also a high-speed performance suitable for video conversion applications. The proposed super-resolution adopts self-similarity framework, which reconstructs the high-frequency (HF) information of the high-resolution image by referring to the image pairs generated from self-similar regions. The method further enhances the perceptual sharpness of the video through region-adaptive HF enhancement algorithm and applies iterative back projection to maintain its consistency with the input image. The proposed method is suitable for parallel processing and therefore is able to provide its superb visual quality on a high conversion speed through GPU-based acceleration. The experimental results show that the proposed method has superior HF reconstruction performance compared to other state-of-the-art upscaling solutions and is able to generate videos that are visually as sharp as the original high-resolution videos. On a single PC with four GPUs, the proposed method can convert Full HD resolution video into UHD resolution with real-time conversion speed. Due to its fast and high-quality conversion capability, the proposed method can be applied on various consumer products such as UHDTV, surveillance system, and mobile devices.