A modified vector quantization based image compression technique using wavelet transform

Jayanta Kumar Debnath, Newaz Muhammad Syfur Rahim, Wai Keung Fung

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddCyfraniad mewn cynhadleddadolygiad gan gymheiriaid

12 Dyfyniadau (Scopus)

Crynodeb

An image compression method combining discrete wavelet transform (DWT) and vector quantization (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate subbands (ten codebooks are generated using the Self Organizing Feature Map algorithm, which are then used in Vector Quantization, of the wavelet transformed subband images, i.e. one codebook for one subband). These subbands are then vector quantized. VQ indices are Huffman coded to increase the compression ratio. A novel iterative error correction scheme is proposed to continuously check the image quality after sending the Huffman coded bit stream of the error codebook indices through the channel so as to improve the peak signal to noise ratio (PSNR) of the reconstructed image. Ten error codebooks (each for each subband of the wavelet transformed image) are also generated for the error correction scheme using the difference between the original and the reconstructed images in the wavelet domain. The proposed method shows better image quality in terms of PSNR at the same compression ratio as compared to other DWT and VQ based image compression techniques found in the literature. The proposed method of image compression is useful for various applications in which high quality (i.e. high precision) are critical (like criminal investigation, medical imaging, etc).

Iaith wreiddiolSaesneg
Teitl2008 International Joint Conference on Neural Networks, IJCNN 2008
Tudalennau171-176
Nifer y tudalennau6
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 26 Medi 2008
Cyhoeddwyd yn allanolIe
Digwyddiad2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, Tsieina
Hyd: 1 Meh 20088 Meh 2008

Cynhadledd

Cynhadledd2008 International Joint Conference on Neural Networks, IJCNN 2008
Gwlad/TiriogaethTsieina
DinasHong Kong
Cyfnod1/06/088/06/08

Dyfynnu hyn