Yih-Fang Huang

Turbo codes for image transmission-a joint channel and source decoding approach

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.

On the application of turbo codes to the robust transmission of compressed images

Compressed images transmitted over noisy channels are extremely sensitive to bit errors. This necessitates the application of error control channel coding to the compressed representation before transmission. This paper presents an image transmission system which takes advantage of the superior performance of turbo codes, an important new class of parallel concatenated codes. Several aspects of the application of turbo codes to image transmission are studied, including comparison to a previous image transmission system using convolutional codes. Experimental results for several channel signal-to-noise ratios show that, in the same SNR range, turbo codes achieve much better performance with less decoding complexity than convolutional codes and that similar performance can be achieved at much lower channel SNRs. Studies also show that the use of feedback from an outer Reed-Solomon code to aid turbo decoding results in further improvement.

Boundary matching detection for recovering erroneously received VQ indexes over noisy channels

This paper addresses the issue of transmitting and reconstructing vector quantization (VQ) coded images over a noisy channel. It presents a novel approach which exploits the spatial contiguity and interpixel correlation of image data sequences. Specifically, a boundary-matching-based detection algorithm (BMDA) is proposed along with a principal-component-splitting-based indexing (PCSBI) scheme which organizes the codebook in a way similar to the GLA-splitting-rule-based indexing scheme, so that some bits of an index are more important than others. Simulation results show that the proposed scheme yields significantly better visual quality and higher signal-to-noise ratio (SNR) than a maximum a posteriori (MAP) detection-based scheme, and has robust performance. It is also shown that incorporation of PCSBI with BMDA greatly reduces the detection complexity, thus facilitating the implementation of BMDA.

Improved decoding of compressed images received over noisy channels

This paper presents an image communication system with improved decoding of compressed image information. A convolutional code protects the compressed image information from channel noise while a Reed-Solomon outer code gives additional protection to the critical image header information. A post-processor detects uncorrected channel errors in the reconstructed image and feeds error location information to a list-based iterative trellis decoder. This list-based decoder provides significant improvement in image quality. Experimental results are given for varying channel SNR and for varying bit rate.

Joint channel and source decoding for vector quantized images using turbo codes

This paper presents a novel joint decoding scheme for image transmission over noisy channels. The proposed decoding scheme includes two features of interaction between a turbo channel decoder and a vector quantization source decoder. First, the source decoder makes use of the channel soft outputs for better reconstruction; second, a feedback algorithm is designed for using source information to improve the error correction capability of the turbo decoder. Significant improvement in terms of the error correction capability and the quality of reconstructed images is achieved compared to a separately operating decoding system.

Fine timing synchronization using power delay profile for OFDM systems

Timing synchronization is more critical in orthogonal frequency division multiplexing (OFDM) systems than in single carrier systems. In this paper, a timing estimation scheme is derived from maximum likelihood estimation (MLE) using power delay profile. It is implemented using a delay locked loop (DLL) to achieve fine timing synchronization for OFDM systems. With this scheme, the mean-square error of symbol timing estimation is decreased by orders of magnitude when compared to the simple peak-finding scheme in multipath fading channels. This scheme also shows higher tracking accuracy for symbol timing drift caused by the sampling clock frequency offset.

On the tradeoff between source and channel coding rates for image transmission

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.

Joint decoding of turbo codes for subband coded image

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.

Contrast enhancement of missile video sequences via image stabilization and product correlation

An image sequence containing a missile in flight often contains a strong plume signature expelled by the missile along with a weak signal corresponding to the missile hardbody. Enhancement of the missile signal is necessary to accurately track the trajectory throughout the image sequence. A parametric motion estimation algorithm is proposed for the passive stabilization of the data set. By stabilizing the image sequence with respect to the missile vacuum core, a registered sequence is produced with the missile located in the same position within each frame. Missile contrast can then be enhanced by applying a novel technique known as product correlation to the stabilized data. Product correlation is presented in the context of higher order statistics, and it offers a computationally efficient means of obtaining sample moments of high orders collectively. Simulations with missile sequences acquired in the IR and visible portions of the spectrum show that image stabilization followed by product correlation successfully enhances missile signal contrast, particularly in image sequences characterized by low SNRs.

A pyramidal image coder using generalized rank-ordered prediction filter

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.