A Hybrid Image Compression Technique Using Neural Network and Vector Quantization With DCT Mohamed El Zorkany Electronics Department, National Telecommunication Institute (NTI), Cairo, Egypt M_zorkany@nti.sci.eg Summary. Image and video transmissions require particularly large band- width 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 hy- brid approaches to image compression are discussed. Hybrid coding of images, in this research, deals with combining three approaches to enhance the indi- vidual methods and achieve better quality reconstructed images with higher compression ratio. In this paper A new Hybrid neural-network, vector quan- tization and discrete cosine transform compression method is presented. This scheme combines the high compression ratio of Neural network (NN) and Vec- tor Quantization (VQ) with the good energy-compaction property of Discrete Cosine Transform (DCT). In order to increase the compression ratio while pre- serving 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. Simula- tion 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. 1 Introduction Day by day the use of multimedia, images and videos are rapidly increasing in a variety application. Type of technique that is used to store in multi- media data is an important although storage is bigger than ever, however it is not enough. Hence, the data compression particularly the image compres- sion plays a vital role. Image compression is a technique for image data rate reduction to save storage space. In other words, the purpose of image com- pression is to reduce the amount of data and to achieve low bit rate digital representation without perceived loss of image quality. Since it was an area R.S. Choraś (ed.), Image Processing and Communications Challenges 5, 233 Advances in Intelligent Systems and Computing 233, DOI: 10.1007/978-3-319-01622-1_28, © Springer International Publishing Switzerland 2014