Image/video compression and quality assessment based on wavelet transform

Because of the contradiction of the vast data size of raw digital images and videos and the limited transmission bandwidth and storage space, it is essential to develop compression methodologies with high compression ratio and good reconstructed quality. It is also important to develop quality metrics which are consistent with human vision and easy to calculate. The spatial-frequency localization and multi-resolution capabilities of the wavelet transform make it a natural means of signal representation. This work investigates the advantages of the wavelet transform and focuses on the following research topics: (1) An image quality metric that assesses the quality of an image in the wavelet domain. (2) A quality constrained compression algorithm that compresses an image to a desired visual quality. (3) An innovative DWT-based temporal filtering scheme that achieves high compression ratio and reduces the ghost effect without motion estimation. (4) A virtual sub-object video coding scheme that is suitable for applications with static background.