Summary
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fiorucci, F., Baruffa, G., Frescura, F.: Objective and subjective quality assessment between JPEG XR with overlap and JPEG 2000. Journal of Visual Communication and Image Representation 23(6), 835–844 (2012)
Au, K.M., Law, N.F., Siu, W.C.: Unified feature analysis in JPEG and JPEG 2000-compressed domains. Journal of Pattern Recognition 40, 2049–2062 (2007)
Li, Drew: Fundamentals of Multimedia. In: Image Compression Standards, ch. 9. Prentice Hall (2003)
Jiang, J.: Image compression with neural networks - A survey. Signal Processing: Image Communication 14(9), 737–760 (1999)
Dokur, Z.: A unified framework for image compression and segmentation by using an incremental neural network. Expert Systems with Applications 34(1), 611–619 (2008)
Sasazaki, K., Saga, S., Maeda, J., Suzuki, Y.: Vector quantization of images with variable block size. Applied Soft Computing 8, 634–645 (2008)
Esakkirajan, S., Veerakumar, T., Murugan, V.S., Navaneethan, P.: Image Compression Using Hybrid Vector Quantization. International Journal of Signal Processing 4 (2008)
Tseng, H., Chang, C.: A Very Low Bit Rate Image Compressor Using Transformed Classified Vector Quantization. Informatica 29, 335–341 (2005)
Robinson, J., Kecman, V.: Combining Support Vector Machine Learning With the Discrete Cosine Transform in Image Compression. IEEE Transactions on Neural Network 14(4) (2003)
Jiang, J.: Image compression with neural networks - A survey. Signal Processing: Image Communication 14(9), 737–760 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
El Zorkany, M. (2014). A Hybrid Image Compression Technique Using Neural Network and Vector Quantization With DCT. In: S. Choras, R. (eds) Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing, vol 233. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01622-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-01622-1_28
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-01621-4
Online ISBN: 978-3-319-01622-1
eBook Packages: EngineeringEngineering (R0)