A Hybrid Image Compression Technique Using Neural Network and Vector Quantization With DCT

TLDR: A new Hybrid neural-network, vector quantization and discrete cosine transform compression method is presented, which combines the high compression ratio of Neural network and Vector Quantization with the good energy-compaction property of Discrete Cosine Transform.

Abstract: Image and video transmissions require particularly large bandwidth and storage space. Image compression technology is therefore essential to overcome these problems. Practically efficient compression systems based on hybrid coding which combines the advantages of different methods of image coding have also being developed over the years. In this paper, different hybrid approaches to image compression are discussed. Hybrid coding of images, in this research, deals with combining three approaches to enhance the individual methods and achieve better quality reconstructed images with higher compression ratio. In this paper A new Hybrid neural-network, vector quantization and discrete cosine transform compression method is presented. This scheme combines the high compression ratio of Neural network (NN) and Vector Quantization (VQ) with the good energy-compaction property of Discrete Cosine Transform (DCT). In order to increase the compression ratio while preserving decent reconstructed image quality, Image is compressed using Neural Network, then take the hidden layer outputs as input to re-compress it using vector quantization (VQ), while DCT was used the code books block. Simulation results show the effectiveness of the proposed method. The performance of this method is compared with the available jpeg compression technique over a large number of images, showing good performance.