image coding can be implemented through DPCM, transform, hybrid, or segmentation coding techniques. Some transform coding techniques, such as cosine and Hadamard, have been exhaustively analyzed and evaluated, while o...
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image coding can be implemented through DPCM, transform, hybrid, or segmentation coding techniques. Some transform coding techniques, such as cosine and Hadamard, have been exhaustively analyzed and evaluated, while others, such as Legendre, have not. This paper introduces the use of Legendre transform in image coding. The transform matrix for different block sizes is calculated, the fast algorithm is derived, and the performance is evaluated through both mean square error and subjective quality. The results obtained have indicated that the system performance is comparable with that of optimum KLT and cosine transforms;moreover, it is simpler in implementation.
In this brief we propose a multisample sparse representation (MSR)-based online dictionary-learning approach to encode images more efficiently. To minimize the reconstructed error while handling a variety of image sam...
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In this brief we propose a multisample sparse representation (MSR)-based online dictionary-learning approach to encode images more efficiently. To minimize the reconstructed error while handling a variety of image samples, we develop a multisample sparse representation method capable of obtaining sparser coefficients combined with learning dictionaries on-the-fly. With a well-learned dictionary, we further derive an MSR-based image coding approach to encode the quantized sparse coefficients with reduced reconstructed errors. Experimental results demonstrate rapid convergence of the proposed dictionary-learning algorithm and improved rate-distortion performance over other competitive image compression schemes both subjectively and quantitatively, validating the effectiveness of the proposed approach.
The purpose of this paper is to study adaptive and fixed predictive coding of digitized images using novel two-dimensional (2-D) multiplicative autoregressive (MAR) models. The stability criteria for both 2-D predicti...
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The purpose of this paper is to study adaptive and fixed predictive coding of digitized images using novel two-dimensional (2-D) multiplicative autoregressive (MAR) models. The stability criteria for both 2-D predictive quantizers as well as 2-D MAR image models with causal nonsymmetric half-plane supports are presented. Three different coding schemes are introduced and their performances are evaluated using extensive computer simulations. The results of these studies demonstrate that for coding of digitized images, MAR model based predictive coders offer an effective alternative to currently available techniques.
image coding methods based on adaptive wavelet transform and those employing zerotree quantization have been shown to be successful in recent years. In this paper, we present a general zerotree structure for an arbitr...
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image coding methods based on adaptive wavelet transform and those employing zerotree quantization have been shown to be successful in recent years. In this paper, we present a general zerotree structure for an arbitrary wavelet packet geometry in an image coding framework. A fast basis selection algorithm which uses a Markov chain based cost estimate of encoding the image using this structure is developed. As a result, our adaptive wavelet zerotree image coder has a relatively low computational complexity, performs comparably to the state-of-the-art image coders, and is capable of progressively encodingimages.
This paper presents an image coding method, based on wavelet transform, where the distribution of the subband coefficients is assumed to be generalized Gaussian. The shape factor and the standard deviation are estimat...
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ISBN:
(纸本)0819448141
This paper presents an image coding method, based on wavelet transform, where the distribution of the subband coefficients is assumed to be generalized Gaussian. The shape factor and the standard deviation are estimated in all subbands. A procedure of bit allocation distributes then the available bitrate to the retained coefficients. The multiple scale leader lattice vector quantization (MSLLVQ) has shown its superiority compared to other structured quantization schemes and now we propose its use for the quantization of the wavelet coefficients. The main contribution of the paper is the procedure for selecting the structure and the leaders for the MSLLVQ. An iterative construction of the MSLLVQ scheme is presented along with the derivation of the operational rate-distortion function. The bit allocation procedure is based on the exponential fitting of the operational rate-distortion curve. The results in terms of peak signal to noise ratio are compared to other image codecs from the literature, the advantage of such a coding structure being particularly important for the fixed rate encoding.
The fractal image compression technique models a natural image using a contractive mapping called fractal mapping in the image space. In this paper, we demonstrate that the fractal image coding algorithm is compatible...
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The fractal image compression technique models a natural image using a contractive mapping called fractal mapping in the image space. In this paper, we demonstrate that the fractal image coding algorithm is compatible with other image coding methods. In other words, we can encode only part of the image using fractal technique and model the remaining part using other algorithms. According to such an idea, a new mapping in the image space called partial fractal mapping is proposed. Furthermore, a general framework of fractal-based hybrid image coding encoding/decoding systems is presented. The framework provides us with much flexibility for real implementations. Many different hybrid image coding schemes can be derived from it. Finally, a new hybrid image coding scheme is proposed where non-fractal coded regions are used to help the encoding of fractal coded regions. Experiments show that the proposed system performs better than the quadtree-based fractal image coding algorithm and the JPEG image compression standard at high compression ratios larger than 30. (C) 2000 Elsevier Science B.V. All rights reserved.
The wide usage of small satellite imagery, especially its commercialization, makes data-based onboard compression not only meaningful but also necessary in order to solve the bottleneck between the huge volume of data...
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The wide usage of small satellite imagery, especially its commercialization, makes data-based onboard compression not only meaningful but also necessary in order to solve the bottleneck between the huge volume of data generated onboard and the very limited downlink bandwidth. In this paper, we propose a method that encodes different regions with different algorithms. We use three shape-adaptive image compression algorithms as the candidates. The first one is a JPEG-based algorithm, the second one is based on the object-based wavelet transform (OWT) method proposed by [1], and the third adopts Hilbert scanning of the regions of interest followed by one-dimensional (1-D) wavelet transform. The three algorithms are also applied to the full image so that we can compare their performance on a whole rectangular image. We use eight Landsat Th I multispectral images and another 12 small satellite single-band images as our data set. The results show that these compression algorithms have significantly different performance for different regions. For relatively smooth regions, e.g., regions that consist of a single type of vegetation or water areas etc, the 1-D wavelet method is the best. For highly textured regions, e.g., urban areas, mountain areas, and so on, the modified OWT method wins over the others. For the whole image, OWT working at whole image mode, which is just an ordinary 2-D wavelet compression, is the heat. Based on this, we propose a new data-based compression architecture that extracts particular regions according to the application of interest and then involves different algorithms to encode different regions in order to achieve better performance than traditional onboard compression schemes in which a fixed compression method is applied to the whole image no matter what the application is. This approach is most appropriate for use with images captured by microsatellites, which are commissioned for specific applications in which one knows a priori which cla
Embedded zerotree wavelet (EZW) algorithm is the well-known effective coding technique for low-bit-rate image compression. In this study, the authors propose a modification of this algorithm, namely new enhanced EZW (...
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Embedded zerotree wavelet (EZW) algorithm is the well-known effective coding technique for low-bit-rate image compression. In this study, the authors propose a modification of this algorithm, namely new enhanced EZW (NE-EZW), allowing to achieve a high compression performance in terms of peak-signal-to-noise ratio and bitrate for lossy image compression. To distribute probabilities in a more efficient way, the proposed approach is based on increasing the number of coefficients not to be encoded by the use of new symbols. Furthermore, the proposed method optimises the binary coding by the use of the compressor cell operator. Experimental results demonstrated the effectiveness of the proposed scheme over the conventional EZW and other improved EZW schemes for both natural and medical image coding applications. They have also shown that the proposed approach outperforms the most well-known algorithms, namely set partitioning in hierarchical trees (SPIHT) and JPEG2000.
This paper describes a new design method for lapped orthogonal transforms (LOTS) that can provide a desired tradeoff between coding efficiency and resilience to transmission errors. Traditionally, LOT bases have been ...
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This paper describes a new design method for lapped orthogonal transforms (LOTS) that can provide a desired tradeoff between coding efficiency and resilience to transmission errors. Traditionally, LOT bases have been designed to maximize the coding efficiency solely. When certain coefficients are lost due to impairments in the transmission channel, the reconstructed image is often unsatisfactory. Previously, we have developed a maximally smooth recovery method for the reconstruction of images from incomplete LOT coefficients. The reconstruction quality depends on the LOT basis used. In this paper, we describe a new LOT-basis design method, which maximizes the weighted average of a coding gain and a reconstruction gain, with the latter being defined according to the maximally smooth recovery method. A coder using the designed basis with a high weighting factor toward the reconstruction gain can achieve significantly better reconstruction quality than a LOT basis that is designed to optimize the coding efficiency only. The newly designed bases are evaluated by their redundancy-rate-distortion performance. Simulation results show that the new bases are more efficient than the bases designed previously by Hemami, in that the new bases require fewer redundancy bits to achieve the same reconstruction quality under the same channel error pattern.
In this paper a morphological subband decomposition with perfect reconstruction is proposed. Critical subsampling is achieved. Reconstructed images using this decomposition do not suffer from any ringing effect. In or...
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In this paper a morphological subband decomposition with perfect reconstruction is proposed. Critical subsampling is achieved. Reconstructed images using this decomposition do not suffer from any ringing effect. In order to avoid poor texture representation by morphological filters an adaptive subband decomposition is introduced. It chooses linear filters on textured regions and morphological filters otherwise. A simple and efficient texture detection criterion is proposed and applied to the adaptive decomposition. Comparisons to other coding techniques such as JPEG and linear subband coding show that the proposed scheme performs significantly better both in terms of PSNR and visual quality.
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