In this paper, an adaptive joint source and channel decoding method is designed to accelerate the convergence of the iterative log-dimain sum-product decoding procedure of LDPC codes as well as to improve the reconstructed image quality. Error resilience modes are used in the JPEG2000 source codec, which makes it possible to provide useful source decoded information to the channel decoder. After each iteration, a tentative decoding is made and the channel decoded bits are then sent to the JPEG2000 decoder. Due to the error resilience modes, some bits are known to be either correct or in error. The positions of these bits are then fed back to the channel decoder. The log-likelihood ratios (LLR) of these bits are then modified by a weighting factor for the next iteration. By observing the statistics of the decoding procedure, the weighting factor is designed as a function of the channel condition. That is, for lower channel SNR, a larger factor is assigned, and vice versa. Results show that the proposed joint decoding methods can greatly reduce the number of iterations, and thereby reduce the decoding delay considerably. At the same time, this method always outperforms the non-source controlled decoding method up to 5dB in terms of PSNR for various reconstructed images.
This study investigates the design and performance of a spatial domain image encoding scheme that adapts to the localized statistical structure of an image. An adaptive differential pulse code modulation (DPCM) image coding system operates on an image that has been preprocessed into segments of variable size, square blocks. Each block is separately encoded by a DPCM system whose parameters have been obtained based upon an underlying nonstationary image model fitted to the block. The source coding performance of the adaptive DPCM algorithm proposed in this study has ben found to result in an improvement of 2.5 dB, or greater, compared to that obtained using a non-adaptive, conventionally designed DPCM encoder/decoder pair when operating at low bit rates. Reconstructed images obtained in this study are of perceptually higher-quality due to the adaptive encoding system design being based on the more realistic assumption of nonstationary statistics. Specifically, experiments have revealed that reconstructed edges within local regions of the image are sharper providing an overall improvement in a viewers subjective assessment of global image quality.
An adaptive discrete cosine transform (DCT) image coding system is implemented with the same average distortion designated for each variable size image block. The variable block size segmentation is performed using a quadtree data structure by dividing the perceptually more important regions of an image into smaller size blocks compared to the size of blocks containing lesser amounts of spatial activity. Due to the nonstationarity of real-world images, each image block is described by a space-varying nonstationary Gauss-Markov random field. The space-varying autoregressive parameters are estimated using an on-line modified least- squares estimator. For each assumed space-varying nonstationary image block, a constant average distortion is assigned and the code rate for each image block is allowed to vary in order to meet the fixed distortion criterion. Simulation results show that reconstructed images coded at low average distortion, based on an assumed space-varying nonstationary image model, using variable size blocks and with variable bit rate per block possess high-quality subjective (visual) and objective (measured) quality at low average bit rates. Performance gains are achieved due to the distortion being distributed more uniformly among the blocks as compared with fixed-rate, stationary image transform coding schemes.
A new algorithm for designing differential pulse code modulation (DPCM) systems is presented for image data compression. When transmitting images over noiseless channels, the distortion between the original and reconstructed images results primarily from quantization noise. This is true when optimal predictor structures are employed. The quantization error becomes severe at low bit rates. This is because of the large quantization error being directly fed back into the predictor and used in subsequent estimation of future pixels. The DPCM scheme developed attempts to balance between nonoptimal predictor designs and significantly reduced feedback effects resulting from quantization errors with the objective of maximizing reconstructed image quality. DPCM system performance using the algorithm is about 2.5 dB greater than that obtained from an optimally designed conventional system. In addition, the algorithm is robust. Thus, the DPCM predictor does not need to be redesigned using exact statistics of the input image data for each image to be transmitted.
Automatic shape recognition using morphological operators has proved to be an effective approach to the problem of shape recognition. We present the problem of shape recognition in noisy environments as that of the problem of recognizing imperfect shapes. The method we present in this paper does not require the use of all possible variations of a shape. Instead, this method employs a priori known shape information as a basis for structuring elements, transforms objects into structuring elements, then uses the structuring elements in a hit-or-miss operation to find the location of the shape being recognized. The choice of structuring elements is critical. The resulting image after the hit or-miss operation contains a set of points which indicate the locations of the target shape. Each occurrence of this target shape is represented by one point, or a small cluster of points within a known disk. A number of examples illustrating the process of recognizing imperfect shapes show that, even though the noise environment changes the appearance of the shapes to be recognized in images, our method provides a fast and accurate solution.
A new algorithm for designing DPCM systems is proposed for image data compression. When transmitting images
over noiseless channels, the distortion between the original and reconstructed images is primarily due to quantization
noise. This is true when optimal predictor structures are employed. The quantization error becomes severe at low bit
rates. This is due to the large quantization error being directly fed back into the predictor and used in subsequent
estimation of future pixels. The DPCM scheme developed attempts to balance between non-optimal predictor designs
and significantly reduced feedback effects due to quantization errors with the objective of maximizing reconstructed
image quality. DPCM system performance using the algorithm is about 2.5 dB greater than that obtained from an
optimally designed conventional system. In addition, the algorithm is robust. Thus, the DPCM predictor does not
need to be re-designed using exact statistics of the input image data for each image to be transmitted.