Title :
Improved MPEG-4 still texture image coding under noisy environment
Author :
Chan, Tommy C L ; Hsung, Tai-Chiu ; Lun, Daniel Pak-Kong
Author_Institution :
Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., China
fDate :
5/1/2003 12:00:00 AM
Abstract :
This paper describes the performance of the MPEG-4 still texture image codec in coding noisy images. As will be shown, when using the MPEG-4 still texture image codec to compress a noisy image, increasing the compression rate does not necessarily imply reducing the peak-signal-to-noise ratio (PSNR) of the decoded image. An optimal operating point having the highest PSNR can be obtained within the low bit rate region. Nevertheless, the visual quality of the decoded noisy image at this optimal operating point is greatly degraded by the so-called "cross" shape artifact. In this paper, we analyze the reason for the existence of the optimal operating point and the "cross" shape artifact when using the MPEG-4 still texture image codec to compress noisy images. We then propose an adaptive thresholding technique to remove the "cross" shape artifact of the decoded images. It requires only a slight modification to the quantization process of the traditional MPEG-4 encoder while the decoder remains unchanged. Finally, an analytical study is performed for the selection and validation of the threshold value used in the adaptive thresholding technique. It is shown that, the visual quality and PSNR of the decoded images are much improved by using the proposed technique comparing with the traditional MPEG-4 still texture image codec in coding noisy images.
Keywords :
adaptive signal processing; data compression; image coding; image texture; noise; PSNR; adaptive thresholding technique; codec; compression rate; cross shape artifact; decoded image; improved MPEG-4 still texture image coding; noisy environment; noisy images; optimal operating point; peak-signal-to-noise ratio; quantization process; visual quality; Codecs; Decoding; Image coding; MPEG 4 Standard; Noise reduction; Noise shaping; PSNR; Shape; Signal to noise ratio; Working environment noise;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2003.810591