This study is to improve double predictor differential pulse code modulation (DP-DPCM) algorithm for image data compression. A variable block-size double predictor DPCM (VBDP-DPCM) image coding system operates on an image that has been preprocessed into segments of variable size, square blocks, and each block is separately encoded by a DP-DPCM system. Quadtree segmentation algorithm is utilized to divide a given real-world image into variable size image blocks. The detail regions of a given image is segmented into blocks with smaller block size, and the background regions of the image will be assigned larger block size to the image blocks. After quadtree segmentation procedure, the differential values between the nearby pixels within an image block are reduced. Therefore, we can decrease the distribution range of the prediction error as well as reduce the quantization levels and the bit rate. We then adopt the double predictor DPCM image coding system to reduce the effect from the fed-back quantization error and not to increase the system complexity. The source coding performance of this proposed variable block-size DP-DPCM image encoder/decoder system is about 5 dB (or greater) coding gain in Signal-to-Noise Ratio than that of a conventional DPCM system.
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.
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