Perceptual wavelet coding and quality assessment for still image

This thesis investigates the use of human perceptual models to improve the performance of image compression and to provide objective quality measures more meaningful than the traditional mean-square-error (MSE) or the peak signal-to-noise ratio (PSNR).; A perceptual wavelet coder is developed to satisfy a wide range of requirements, from perceptually lossless quality to high compression ratio. A perceptual model is designed to allow the coder to allocate bits for each subband based on minimizing the overall perceptual distortion. An option is included to allow region of interest be compressed at desired perceptual quality. The coder achieves scalability by multi-rate quantization and entropy coding. Results demonstrated better performance of the perceptual wavelet coder than JPEG coder and wavelet coder without perceptual model.; A vision model is implemented for perceptual quality assessment. The model showed fairly accurate prediction about where the distortion is more noticeable by human eyes. The numerical measures generated from the model showed more accurate assessment than MSE and PSNR.