فشرده سازي تصوير به كمك تبديل چند لايه ي Wavelet-Contourlet

چكيده انگليسي :

Compression of Digital Images Using Multilayered Wavelet Contourlet Method Hossein Talebi Esfandarani h talebi@ec iut ac ir March 15 2010 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi S Samavi Prof Supervisor samavi96@cc iut ac ir M R Ahmadzadeh Assist Prof Supervisor ahmadzadeh@cc iut ac ir Abstract Considering progress in wireless and network telecommunication need for digital image compression is inevitable There are many methods in image compression which some of them have acceptable results Lossy image compression algorithms are applicable whenever the exact reconstruction of an image is not expected These algorithms are usually based on transform methods A traditional scheme to realize multi resolution image representation MIR is to apply 1 D filters separately in horizontal and vertical directions commonly referred to as separable transform In contrast non separable transforms consist of 2 D filters and 2 D downsampling matrices which cannot be factorized into 1 D filter downsampling pairs The traditional wavelet transform WT is categorized as a separable transform which is used in various applications such as compression noise removal image edge enhancement and feature extraction Contours are abundant in natural images and cannot be categorized as either horizontal or vertical edges However wavelet has poor diagonal orientation selectivity since frequencies with different orientations are gathered into one subband in each resolution For example in image coding for low bit rates reconstructed images often have blurred regions Combination of separable and non separable filter banks have been applied to reduce these artefacts Dividing image in to homogeneous and heterogeneous regions is a new method which is employed recently In this method each layer of image is compressed by the best transform The contourlet transform is used to extract curves in texture areas This transform employs laplacian pyramid and directional filter banks to take out contours and curves which aren t detected completely through other transforms like wavelet The only problem of the contourlet is its redundancy which is a bottleneck for low bit rate compression purposes In this thesis we propose new compression methods to avoid this problem We first introduce contourlet transform and show our simulation results Then we suggest multilayered methods for compression in low bit rate In these methods the first transform is wavelet which removes texture regions from first layer image To compress second layer we used contourlet to extract curves and directional edges We showed that images that are compressed and reconstructed by our method at low bit rates have good qualities both visually and in terms of the produced PSNRs Key Words image compression multilayer methods wavelet contourlet structure tensor

طالبي اسفنداراني، حسين

فشرده سازي تصوير به كمك تبديل چند لايه ي Wavelet-Contourlet

موجك , كانتورلت , Structure tensor