In this paper, a genetic algorithm-based image compression technique using pattern classification is introduced. From one hand, the block pattern coding technique classifies image blocks into low-detailed and high-detailed blocks and codes the individual blocks according to their types. On the other hand, a genetic algorithm technique explores a given search space in parallel by means of iterative modification of a population of potenial solutions. The GA operation described here, searches for the optimal threshold(s) for the bi-level or multi level quantization of high detailed image blocks. Comparison of the results of the proposed method with the coding algorithms based on the two level minimum mean square error quantizer reveal that the former method can almost achieve optimal quantization with much less computation than required in the latter case.
A new quadtree segmented predictive image coding technique is presented in this paper for exploiting the correlation between adjacent image blocks and uniformity in variable block size image blocks. For exploiting correlation between adjacent image blocks, a predictive coding technique is used for reducing the inter block correlation. The proposed quadtree technique decomposes an image into variable size block, and each segmented block is then coded at a different bit rate according to the level of visual activity inside the block. A novel classification scheme, which operates based on
the distribution of the block residuals, is employed to determine the activity level inside the block. In this method, the orientation of the pattern appearing inside the block will be computed as an aid to the classification. To preserve edge integrity, a block pattern based coding technique is proposed and incorporated to the predictive coding method for coding the high-activity blocks of the segmented image. The use of a set of parameters associated with the pattern
appearing inside a high activity block at the receiver, together with the inter block correlation information, reduce the cost of instruction and saves the encoding time. Experiments have been conducted to compare with other predictive and quadtree- based techniques. Results show a lower bit rate at competitive reconstruction quality.
A new image compression approach is proposed in which variable block size technique is adopted, using quadtree decomposition, for coding images at low bit rates. In the proposed approach, low-activity regions, which usually occupy large areas in an image are coded with a larger block size and the block mean is used to represent each pixel in the block. A novel classification scheme, which operates based on the distribution of the block residuals is employed to determine whether the processed block is a low-detail or a high-detail block. To preserve edge integrity, a new edge-based coding technique is used to code high-activity regions. In this method, the orientation of edge pattern within a high-activity block will be computed as an aid to the classification. A novel edge-oriented classifier, operating based on the histogram analysis of the pixels' orientations, is also proposed to for edge classification. Each edge block is represented by a set of parameters associated with the pattern appearing inside the block. The use of these parameters at the receiver reduces the cost of reconstruction significantly and exploits the efficiency of the proposed technique. Experiments have been conducted to compare with variance-based quadtree technique, vector quantization-based variable size algorithms, as well as the standard JPEG. Results show higher PSNR at competitive reconstruction quality.
In this paper, we present a wavelet-based image compression technique, which incorporates some of the human visual system (HVS) characteristics for the wavelet decomposition and bit allocation of subband images. The wavelet coefficients are coded using a new technique, referred to as Block Pattern Coding algorithm. The proposed technique employs a set of local geometric patterns, which preserve the underlying edge geometries in the high frequency signals at very low coding rates. Critical to the success of our approach is the frequent utilization of a special block pattern, which is a uniform pattern of constant intensity to reproduce image blocks of near constant intensity. A performance comparison with JPEG and HVS-based wavelets using VQ is presented for both moderate and heavy compression.
In this paper, we present a novel edge-based coding algorithm for image compression. The proposed coding scheme is the predictive version of the original algorithm, which we presented earlier in literature. In the original version, an image is block coded according to the level of visual activity of individual blocks, following a novel edge-oriented classification stage. Each block is then represented by a set of parameters associated with the pattern appearing inside the block. The use of these parameters at the receiver reduces the cost of reconstruction significantly. In the present study, we extend and improve the performance of the existing technique by exploiting the expected spatial redundancy across the neighboring blocks. Satisfactory coded images at competitive bit rate with other block-based coding techniques have been obtained.
In this paper, a new image representation scheme using a set of block templates is introduced first. Its application in image coding is presented afterwards. In the proposed representation scheme, a set of block templates is constructed to represent three basic types of image patterns: uniform, edge, and irregular. A novel classifier, which is designed based on the histogram shape analysis of image blocks, is employed to classify the block templates according to their level of visual activity. Each block template is then represented by a set of parameters associated with the pattern appearing inside the block. Image representation using these templates requires considerably fewer bits than the original pixel-wise description and yet characterizes perceptually significant features more effectively. The coding system approximates each image block by one of the block templates and further quantizes the template parameters. Satisfactory coded images have been obtained at bit rates between 0.3 - 0.4 bits per pixel (bpp).
In this paper, a new image coding technique is introduced first. Its inclusion in a pyramidal representation is presented afterwards. In the proposed stand-alone coding algorithm, referred to as Block template Matching, an image is block coded according to the type of individual blocks. A novel classifier, which is designed based on the histogram analysis of blocks is employed to classify the image blocks according to their level of visual activity. Each block is then represented by a set of parameters associated with the pattern appearing inside the block. The use of these parameters at the receiver reduces the cost of reconstruction significantly and exploits the efficiency of the proposed technique. The coding efficiency of the proposed technique along with the low computational complexity and simple parallel implementation of the pyramid approach allows for a high compression ratio as well as a good image quality. Satisfactory coded images have been obtained at bit rates in the range of 0.30 - 0.35 bits per pixel.
An image coding scheme using a set of image visual patterns is introduced. These patterns are constructed to represent two basic types of image patterns (uniform and oriented) over small blocks of an image. The coding system characterizes an image by its local features, and further approximates each image block by a block pattern. Algorithms for pattern classification, computation of pattern parameters, and image reconstruction from these parameters are presented, and these provide the necessary tools for applying the proposed coding method to varius images. Satisfactory coded images have been obtained, and compression ratios in the order of 15 to 1 have been achieved.
In this paper, a DCT based coding technique for adaptive coding of images according to the level of visual activity is presented. Adaptation is based on adaptive quantization and adaptive bit selection. In the proposed system, we initially partition the image into a large number of sub-blocks of 4 X 4 pixels. A novel image analysis may then be performed prior to the coding in order to decide what is the most significant information to encode. Classification according to the activity level within the blocks is based on the local statistics, and is used for adaptive bit selection, whereas optimum quantifiers having Gaussian density are used to achieve adaptive quantization. Satisfactory performance is demonstrated in terms of direct comparison of the original and the reconstructed images.
In this paper, a novel image analysis technique is proposed, which may be performed prior to coding in order to decide what is the most significant information to encode. In the proposed system, the image to be coded is first partitioned into a large number of sub-blocks of N*N pixels. The blocks can then be stored into two major classes according to the level of the visual activity present. The classification is based on analyzing the local histogram within each sub-block. In this paper, we initially analyze the image blocks to separate uniform blocks from those that can be classified as non-uniform blocks. Adjacent uniform blocks with the same statistics are merged to form large blocks. These blocks can then be coded by their mean values. It is also shown that the non-uniform blocks may also be classified into three categories with different levels of activity.