Research Of Image Compression Based On HVS And DWT

With the generalization of computer network and multimedia, video and image play an more and more important part in our everyday life, how to store video and image become an exigency issue. Since 1997, the JPEG(Joint Photographic Expert Group)committee has worked on the new generation of image compression algorithm. A new version of the file format was released at the end of year 2000.In order to distinguish the new JPEG format from the original one and to celebrate the generation of the new file format, it was named JPEG2000.The core skill of JPEG is discrete cosine transform(DCT),whereas the core skill of JPEG2000 is discrete wavelet transform(DWT). In the last decade, discrete wavelet transform(DWT)has proven to be a useful technique for a wide range of applications including signal analysis, signal compression, pattern recognition, biomedicine, and numerical analysis. Since DWT has excellent features of energy compaction and inherent scalability, it has been applied extensively in the field of image and video compression.There are a lot of methods of image compression at present, but they are all based on removing statistical redundancy, rarely consider the human visual redundancy, it is the reason why can not achieve higher compression. Wavelet multi-resolution was widely accepted for the model of human vision, it can described objects from the rough to fine, and give a better perform in compression. integer wavelet transform not only have the characteristic of wavelet transform, but also with the advantages of simple calculation and fast processing. The JND(Just Noticeable Distortion) concept of the visual coding proposed by Jayant, which defined a concept of threshold for visual image signals, signals that less than the threshold can not be perceptible by human visual.Based on the characteristic of HVS(Human Visual System) and DWT (Discrete Wavelet Transform), The paper proposes an approach of coding in image compression based on integer wavelet transform. Compared with the method in references, the proposed method leads to better image quality according to the experiment results with the same condition.