Video compression using Self organizing map and pattern storage using Hopfield Neural Network
Manu Pratap Singh, K. V. Arya, Kajal Sharma · 2009
Video compression is an essential task in video storage and transmission. In this paper, we worked for compressing the video sequence using Self-organizing map (SOM) and then stored the output feature vector of SOM using Hopfield Neural Network. The SOM algorithm is based on unsupervised, competitive learning. It extracts the useful features from video frames by feature extraction method. In our approach, the video sequence is first divided into video frames. The frames are processed by passing to the input of Self-organizing map (SOM) that outputs the informative features by removing some of the redundant information contained in video frames. Self-organizing map is an efficient method for reducing the size of images in the neural network field. The feature vector obtained is further used for storing the patterns using Hopfield network. The feature vector is applied to the input of the Hopfield neural network and stored in the network for encoding the video frames. Thus the compressed video frames are encoded and stored in memory as a codeword. The codeword is used for reconstruction of the video frames by using the same Hopfield network. The codeword is applied to the Self-organizing map network and the video frames are reconstructed from those codeword. Simulation results show that high compression rate is achieved while maintaining the good reconstruction quality of images.