Ronggang Wang

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H.264/AVC is the latest standard for video coding drafted jointly by the ISO/IEC Moving Picture Experts Group and the ITU-T Video Coding Experts Group. H.264/AVC provides up to 50% gains in compression efficiency over a wide range of bit rates and video resolutions compared to previous standards. On the other hand, the decoder complexity is about four times(More)
In order to reduce the bit rate of video signals, motion compensation prediction is applied in modern video coding technology. This is a form of temporal redundancy reduction in which the current coding frame is predicted by a motion compensated prediction from some other already decoded frames according to motion vector. As real motion has arbitrary(More)
This paper proposes a high efficiency memory controller for an H.264 HDTV decoder with synchronous DRAMs. As H.264 adopts tree structured (supports small block size) motion compensation, the bandwidth requirement of an H.264 HDTV decoder is higher than previous video processing algorithms. This requires to be optimized. Based on H.264 decoding data access(More)
In this paper, a novel united low-light image enhancement framework for both contrast enhancement and denoising is proposed. First, the low-light image is segmented into superpixels, and the ratio between the local standard deviation and the local gradients is utilized to estimate the noise-texture level of each superpixel. Then the image is inverted to be(More)
A spatio-temporal autoregressive model is proposed in this paper to address the problem of frame rate up conversion. Every pixel in a skipped frame is generated as a linear combination of pixel values from forward and backward reference frames. At the beginning of the presented scheme, the coarse model parameters are computed according to the given initial(More)
Intermediate view synthesis is a key technique for free-viewpoint video applications. Various methods have been proposed in this filed. However, most of those methods are so complex that they can't be realized for realtime. In addition, a perfect depth image is hard to acquire for the mixtures of foreground pixels and background pixels at the object(More)
In this paper, we propose an efficient super-resolution (SR) method based on deep convolutional neural network (CNN), namely gradual upsampling network (GUN). Recent CNN based SR methods either preliminarily magnify the low resolution (LR) input to high resolution (HR) and then reconstruct the HR input, or directly reconstruct the LR input and then recover(More)