Recently, as devices and communication technologies capable of acquiring digital images such as smart phones and digital cameras are developed, demand for digital images is increasing. However, the noise is generated in the process of acquiring, transmitting, and storing digital images. Therefore, in this paper, we propose an algorithm using an enhanced median filter and a Bitonic filter to reduce high-density impulse noise and preserve the edge region. Experimental results demonstrate that the proposed algorithm outperforms the conventional filtering techniques for high-density impulse noise.
In this paper, we propose codec classification algorithm based on recurrent neural network (RNN) model. In video compression, codecs, such as MPEG2 and H.264/AVC, have their own distinctive data structure. These unique structures which are almost shown in header can be considered their feature. The proposed algorithm exploits that characteristics for classifying unknown bitstreams into specific codec. According to the fact that RNN is appropriate to time series data for learning to classification/recognition, the feature of an encoded bitstream can be extracted. We constitute the encoded bitstream as an input and give the bitstream its label indicating codec index. Two standard codecs, MPEG2 and H.264/AVC, are used in experiment. Experimental results show that the proposed RNN model classified bitstreams into corresponding codecs to some extent.
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