This paper studies an application of turbo codes to compressed image/video transmission and presents an approach to improving error control performance through joint channel and source decoding (JCSD). The proposed approach to JCSD includes error-free source information feedback, error-detected source information feedback, and the use of channel soft values (CSV) for source signal postprocessing. These feedback schemes are based on a modification of the extrinsic information passed between the constituent maximum a posteriori probability (MAP) decoders in a turbo decoder. The modification is made according to the source information obtained from the source signal processor. The CSVs are considered as reliability information on the hard decisions and are further used for error recovery in the reconstructed signals. Applications of this joint decoding technique to different visual source coding schemes, such as spatial vector quantization, JPEG coding, and MPEG coding, are examined. Experimental results show that up to 0.6 dB of channel SNR reduction can be achieved by the joint decoder without increasing computational cost for various channel coding rates.

This paper presents a lossy image compression scheme that employs a generalized rank-ordered prediction filter for pyramidal image coding. The proposed prediction method renders significantly reduced variance of the quantizer input. Consequently, the quality of the decompressed image is much enhanced due to the greatly reduced quantization distortion. Both analytical and simulation results show that the proposed scheme yields high-quality performance.

The joint channel-source decoding scheme for using turbo codes to protect compressed image data proposed by Peng, Huang, Costello and Stevenson (see, Proc. 1998 IEEE International Symposium on Circuits and Systems, Monterey, California, 1998) is modified for subband coded images. Two different modifications are presented and compared. The factors affecting the performance of the schemes are studied. Both modified schemes show superiority over a separate decoding system.

This paper is intended to investigate the bit rate allocation between source coding and channel coding rates when turbo codes are used to protect vector quantized images. The experimental results show that an appropriate bit rate allocation can lead to significant gains in the quality of the reconstructed images compared to an arbitrary bit rate allocation scheme.

This paper presents a lossy image compression scheme which employs a pyramidal coding technique with segmentation based on the local activity levels. A novel prediction method is introduced, rendering significantly reduced variance of the quantizer input. Consequently, the coding efficiency is much enhanced due to the greatly reduced quantization distortion. Both theoretical analysis and simulation results show that the proposed scheme yields high quality performance. A reconstruction PSNR of 30.5 dB is achieved at a bit rate of 0.181 bpp for a 512×512 image "Lenna" before entropy coding.