Image compression using noncausal prediction

Image compression commonly is achieved using prediction of the value of pixels from surrounding pixels. Normally the choice of pixels used in the prediction is restricted to previously scanned pixels. A better prediction can be achieved if pixels on all sides of the pixel to be predicted are used. A prediction and decoding method is proposed that is independent of scanning order of the image. The decoding process makes use of an iterative decoder. A sequence of images is generated that converges to a final image that is identical to the original image. The theory underlying noncausal prediction and iterative decoding is developed. Convergence properties of the decoding algorithm are studied and conditions for convergence are presented. Distortions to the prediction residual after encoding can be caused by storage requirements, such as quantization and compression and also by errors in transmission. Effects of distortions of the residual on the final decoded image are investigated by introducing several types of distortion of the residual, including (1) alteration of randomly selected bits in the residual, (2) addition of a sinusoidal signal to the residual, (3) quantization of the residual and (4) compression of the residual using lossy Haar wavelet coding. The resulting distortion in the decoded images was generally less for noncausal prediction than for causal prediction, both in terms of PSNR and visual quality. Most noticeably, the streaks found in the decoded Image after causal encoding were absent with noncausal encoding

Data compression for satellite images

An efficient data compression system is presented for satellite pictures and two grey level pictures derived from satellite pictures. The compression techniques take advantages of the correlation between adjacent picture elements. Several source coding methods are investigated. Double delta coding is presented and shown to be the most efficient. Both predictive differential quantizing technique and double delta coding can be significantly improved by applying a background skipping technique. An extension code is constructed. This code requires very little storage space and operates efficiently. Simulation results are presented for various coding schemes and source codes

Image Segmentation using Human Visual System Properties with Applications in Image Compression

In order to represent a digital image, a very large number of bits is required. For example, a 512 X 512 pixel, 256 gray level image requires over two million bits. This large number of bits is a substantial drawback when it is necessary to store or transmit a digital image. Image compression, often referred to as image coding, attempts to reduce the number of bits used to represent an image, while keeping the degradation in the decoded image to a minimum. One approach to image compression is segmentation-based image compression. The image to be compressed is segmented, i.e. the pixels in the image are divided into mutually exclusive spatial regions based on some criteria. Once the image has been segmented, information is extracted describing the shapes and interiors of the image segments. Compression is achieved by efficiently representing the image segments. In this thesis we propose an image segmentation technique which is based on centroid-linkage region growing, and takes advantage of human visual system (HVS) properties. We systematically determine through subjective experiments the parameters for our segmentation algorithm which produce the most visually pleasing segmented images, and demonstrate the effectiveness of our method. We also propose a method for the quantization of segmented images based on HVS contrast sensitivity, arid investigate the effect of quantization on segmented images

Investigation of Different Video Compression Schemes Using Neural Networks

Image/Video compression has great significance in the communication of motion pictures and still images. The need for compression has resulted in the development of various techniques including transform coding, vector quantization and neural networks. this thesis neural network based methods are investigated to achieve good compression ratios while maintaining the image quality. Parts of this investigation include motion detection, and weight retraining. An adaptive technique is employed to improve the video frame quality for a given compression ratio by frequently updating the weights obtained from training. More specifically, weight retraining is performed only when the error exceeds a given threshold value. Image quality is measured objectively, using the peak signal-to-noise ratio versus performance measure. Results show the improved performance of the proposed architecture compared to existing approaches. The proposed method is implemented in MATLAB and the results obtained such as compression ratio versus signalto- noise ratio are presented

3-dimensional median-based algorithms in image sequence processing

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1990.Thesis (Master's) -- Bilkent University, 1990.Includes bibliographical references leaves 75-78.This thesis introduces new 3-dimensional median-based algorithms to be used in two of the main research areas in image sequence proc(',ssi,ng; image sequence enhancement and image sequence coding. Two new nonlinear filters are developed in the field of image sequence enhancement. The motion performances and the output statistics of these filters are evaluated. The simulations show that the filters improve the image quality to a large extent compared to other examples from the literature. The second field addressed is image sequence coding. A new 3-dimensional median-based coding and decoding method is developed for stationary images with the aim of good slow motion performance. All the algorithms developed are simulated on real image sequences using a video sequencer.Alp, Münire BilgeM.S

Motion compensated video coding

The result of many years of international co-operation in video coding has been the development of algorithms that remove interframe redundancy, such that only changes in the image that occur over a given time are encoded for transmission to the recipient. The primary process used here is the derivation of pixel differences, encoded in a method referred to as Differential Pulse-Coded Modulation (DPCM)and this has provided the basis of contemporary research into low-bit rate hybrid codec schemes. There are, however, instances when the DPCM technique cannot successfully code a segment of the image sequence because motion is a major cause of interframe differences. Motion Compensation (MC) can be used to improve the efficiency of the predictive coding algorithm. This thesis examines current thinking in the area of motion-compensated video compression and contrasts the application of differing algorithms to the general requirements of interframe coding. A novel technique is proposed, where the constituent features in an image are segmented, classified and their motion tracked by a local search algorithm. Although originally intended to complement the DPCM method in a predictive hybrid codec, it will be demonstrated that the evaluation of feature displacement can, in its own right, form the basis of a low bitrate video codec of low complexity. After an extensive discussion of the issues involved, a description of laboratory simulations shows how the postulated technique is applied to standard test sequences. Measurements of image quality and the efficiency of compression are made and compared with a contemporary standard method of low bitrate video coding

Motion compensated interpolation for subband coding of moving images

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 108-119).by Mark Daniel Polomski.M.S

Motion compensated video coding

The result of many years of international co-operation in video coding has been the development of algorithms that remove interframe redundancy, such that only changes in the image that occur over a given time are encoded for transmission to the recipient. The primary process used here is the derivation of pixel differences, encoded in a method referred to as Differential Pulse-Coded Modulation (DPCM)and this has provided the basis of contemporary research into low-bit rate hybrid codec schemes. There are, however, instances when the DPCM technique cannot successfully code a segment of the image sequence because motion is a major cause of interframe differences. Motion Compensation (MC) can be used to improve the efficiency of the predictive coding algorithm. This thesis examines current thinking in the area of motion-compensated video compression and contrasts the application of differing algorithms to the general requirements of interframe coding. A novel technique is proposed, where the constituent features in an image are segmented, classified and their motion tracked by a local search algorithm. Although originally intended to complement the DPCM method in a predictive hybrid codec, it will be demonstrated that the evaluation of feature displacement can, in its own right, form the basis of a low bitrate video codec of low complexity. After an extensive discussion of the issues involved, a description of laboratory simulations shows how the postulated technique is applied to standard test sequences. Measurements of image quality and the efficiency of compression are made and compared with a contemporary standard method of low bitrate video coding