Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 4 Articles
An improved fast and efficient mode decision method for H.264/AVC intra coding is proposed, which is based on the analysis of\nthe gravity center method and more efficient mode selection. In contrast to the fast mode decision method where the intra modes\nare determined by the gravity center of the block, the mass center vector is computed for the block and the subblocks formed by\nthe proposed subsampling techniques. This method is able to determine all correlation directions of the block that correspond to\nthe intra prediction mode directions of the H.264/AVC. On this basis, only a small number of intra prediction modes are chosen as\nthe best modes for rate-distortion optimization (RDO) calculation. Different video sequences are used to test the performance of\nthe proposed method. Experimental results reveal the significant computational savings achieved with slight peak signal-to-noise\nratio (PSNR) degradation and bit-rate increase...
Since rapid growth of Internet technologies and mobile devices,multimedia data such as images and videos are explosively growing\non the Internet.Managing large scale multimedia data with correct tags and annotations is very important task. Incorrect tags and\nannotations make it hard to manage multimedia data. Accurate tags and annotation ease management of multimedia data and\ngive high quality retrieve results. Fully manual image tagging which is tagged by user will be most accurate tags when the user\ntags correct information. Nevertheless, most of users do not make effort on task of tagging. Therefore, we suffer from lots of noisy\ntags. Best solution for accurate image tagging is to tag image automatically. Robust automatic image tagging models are proposed\nby many researchers and it is still most interesting research field these days. Since there are still lots of limitations in automatic\nimage tagging models, we propose efficient automatic image tagging model using multigrid based image segmentation and feature\nextraction method. Our model can improve the object descriptions of images and image regions. Our method is tested with Corel\ndataset and the result showed that our model performance is efficient and effective compared to other models....
Packet loss will make severe errors due to the corruption of related video data. For most video streams, because the predictive\ncoding structures are employed, the transmission errors in one frame will not only cause decoding failure of itself at the receiver\nside, but also propagate to its subsequent frames along the motion prediction path, which will bring a significant degradation of\nend-to-end video quality. To quantify the effects of packet loss on video quality, a no-reference objective quality assessment model\nis presented in this paper. Considering the fact that the degradation of video quality significantly relies on the video content, the\ntemporal complexity is estimated to reflect the varying characteristic of video content, using the macroblocks with different motion\nactivities in each frame. Then, the quality of the frame affected by the reference frame loss, by error propagation, or by both of them is\nevaluated, respectively. Utilizing a two-level temporal pooling scheme, the video quality is finally obtained. Extensive experimental\nresults show that the video quality estimated by the proposed method matches well with the subjective quality....
To improve the spatial resolution of reconstructed images/videos, this paper proposes a Super resolution (SR) reconstruction\nalgorithm based on iterative back projection. In the proposed algorithm, image matching using critical-point filters (CPF) is\nemployed to improve the accuracy of image registration. First, a sliding window is used to segment the video sequence. CPF based\nimage matching is then performed between frames in the window to obtain pixel-level motion fields. Finally, high-resolution (HR)\nframes are reconstructed based on the motion fields using iterative back projection (IBP) algorithm. The CPF based registration\nalgorithm can adapt to various types of motions in real video scenes. Experimental results demonstrate that, compared to optical\nflow based image matching with IBP algorithm, subjective quality improvement and an average PSNR score of 0.53 dB improvement\nare obtained by the proposed algorithm, when applied to video sequence....
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