Filter banks, subband/wavelets, and multiresolution decompositions that employ recursive filters have been considered previously and are recognized for their efficiency in partitioning the frequency spectrum. This pap...
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Filter banks, subband/wavelets, and multiresolution decompositions that employ recursive filters have been considered previously and are recognized for their efficiency in partitioning the frequency spectrum. This paper presents an analysis of a new infinite impulse response (IIR) filter bank in which these computationally efficient filters may be changed adaptively in response to the input, The new filter bank framework is presented and discussed in the context of subband image coding. In the absence of quantization errors, exact reconstruction can be achieved, By the proper choice of an adaptation scheme, it is shown that recursive linear time-varying (LTV) filter banks can yield improvement over conventional ones.
As the capacity of communication networks grows larger and the speed of data processing increases, various coding methods for handling digital images conveniently are being developed. The DCT-based standard coding met...
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As the capacity of communication networks grows larger and the speed of data processing increases, various coding methods for handling digital images conveniently are being developed. The DCT-based standard coding method generally achieves good compression performance. However, for images containing sharp edges, a high compression ratio causes degradation of the reconstructed edges. In this paper, we propose a method for estimating the high-frequency components from the lower frequency components. This method is derived by applying an auto-regressive (AR) estimation to the DCT coefficients of the edge blocks, which are the subblocks containing sharp edges. The AR coefficients are obtained for each edge block. The image is reconstructed from the AR coefficients, the estimation errors of the high-frequency components, and the other frequency components. The AR coefficients are quantized uniformly and incorporated into the head of the AC component sequences in the JPEG standard method. When the proposed method was applied to some images containing sharp edges such as characters or figures, the coding performance was improved, compared with the JPEG baseline coding method. (C) 1995 Academic Press, Inc.
An adaptive block discrete-cosine transform (DCT) coding scheme is implemented with the same average distortion designated for each block. This constant distortion designation not only has perceptual advantages, but a...
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An adaptive block discrete-cosine transform (DCT) coding scheme is implemented with the same average distortion designated for each block. This constant distortion designation not only has perceptual advantages, but also allows the rate to vary, adjusting to the changing spectral characteristics among the blocks. The successful execution of this scheme requires a different spectral estimate for each block. To keep overhead and computation within limits, a novel technique is introduced by which a two-dimensional block spectrum is characterized by a one-dimensional autoregressive model. Simulations with images of natural scenes and medical radiology provide reconstructions with nearly uniform block distortion and very high visual and measurable quality at low rates.
In this paper, we propose a coding algorithm for still images using vector quantization (VQ) and fractal approximation, in which low-frequency components of an input image are approximated by VQ, and its residual is c...
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In this paper, we propose a coding algorithm for still images using vector quantization (VQ) and fractal approximation, in which low-frequency components of an input image are approximated by VQ, and its residual is coded by fractal mapping, The conventional fractal coding algorithms indirectly used the gray patterns of an original image with contraction mapping, whereas the proposed fractal coding method employs an approximated and then decimated image as a domain pool and uses its gray patterns, Thus, the proposed algorithm utilizes fractal approximation without the constraint of contraction mapping, For approximation of an original image, we employ the discrete cosine transform (DCT) rather than conventional polynomial-based transforms. In addition, for variable block-size segmentation, we use the fractal dimension of a block that represents the roughness of the gray surface of a region, Computer simulations with several test images show that the proposed method shows better performance than the conventional fractal coding methods for encoding still pictures.
The introduction of the fast Fourier transform algorithm has led to the development of the Fourier transform image coding technique whereby the two-dimensional Fourier transform of an image is transmitted over a chann...
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The introduction of the fast Fourier transform algorithm has led to the development of the Fourier transform image coding technique whereby the two-dimensional Fourier transform of an image is transmitted over a channel rather than the image itself. This devlopement has further led to a related image coding technique in which an image is transformed by a Hadamard matrix operator. The Hadamard matrix is a square array of plus and minus ones whose rows and columns are orthogonal to one another. A high-speed computational algorithm, similar to the fast Fourier transform algorithm, which performs the Hadamard transformation has been developed. Since only real number additions and subtractions are required with the Hadamard transform, an order of magnitude speed advantage is possible compared to the complex number Fourier transform. Transmitting the Hadamard transform of an image rather than the spatial representation of the image provides a potential toleration to channel errors and the possibility of reduced bandwidth transmission.
A new general method is proposed far image coding which exploits similarities, possibly with scaling, among different parts of the image, The coding is performed by approximating each image block with a linear combina...
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A new general method is proposed far image coding which exploits similarities, possibly with scaling, among different parts of the image, The coding is performed by approximating each image block with a linear combination of blocks selected from a library of blocks. This library is made up of (1) a set of fixed library blocks, (2) a set of blocks taken from the filtered, subsampled image, and (3) a set of blocks taken from the image without any change of scale, When the last two sets are selected causally, the decoding process is noniterative with no constraints on the coefficients of the library blocks, The index of the selected library blocks and their corresponding coefficients make the code for each range block, Methods are proposed for making the library and selecting blocks from the library. (C) 1997 Academic Press.
This paper presents a fast algorithm of vector quantization for image coding that relies on three conditions of early termination to confine the search space, resulting in acceleration of the encoding process. These t...
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This paper presents a fast algorithm of vector quantization for image coding that relies on three conditions of early termination to confine the search space, resulting in acceleration of the encoding process. These termination conditions are derived based on the gray-level features that are extracted from the individual vectors of pixels. Each incoming vector is compared to the codebook entries first using these fast tests. The codebook entries that fail one or more of the fast tests can be rejected without further consideration. Thus, time-consuming computations of the squared Euclidean distance in the proposed system are performed on only a few codebook entries that first pass all three fast tests. Verified results for the system show over 97% reduction of execution time compared to the full search algorithm and 50% compared to the mean-ordered partial codebook search method in image VQ encoding.
Fast-growing intelligent media processing applications demand efficient processing throughout the processing chain from the edge to the cloud, and the complexity bottleneck usually lies in the parallel decoding of mul...
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Fast-growing intelligent media processing applications demand efficient processing throughout the processing chain from the edge to the cloud, and the complexity bottleneck usually lies in the parallel decoding of multiple-channel compressed bitstreams before analyzing. This occurs because the traditional media coding scheme generates a binary stream without a semantic structure, which is unable to be operated directly at the bitstream level to support different tasks such as classification, recognition, detection, etc. Therefore, in this article, we propose a learning-based semantically structured image coding (SSIC) framework to generate a semantically structured bitstream (SSB), where each part of the bitstream represents a specific object and can be directly used for the aforementioned intelligent tasks. Specifically, we integrate an object location extraction module into the compression framework to locate and align objects in the feature domain. Then, each object together with the background is compressed separately and reorganized to form a structured bitstream to enable the analysis or reconstruction of specific objects directly from partial bitstream. Furthermore, in contrast to existing learning-based compression schemes that train the specific model for a specific bitrate, we share most of the model parameters among various bitrates to significantly reduce the model size for variable-rate compression. The experimental results demonstrate the effectiveness of the proposed coding scheme whose compression performance is comparable to existing image coding schemes, where intelligent tasks such as classification and pose estimation can be directly performed on a partial bitstream without performance degradation, significantly reducing the complexity for analyzing tasks.
In this paper we present a method for motion compensated image coding based upon a two-step displacement estimation procedure. The first step utilizes a maximum a posteriori (MAP) estimator to determine the best integ...
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In this paper we present a method for motion compensated image coding based upon a two-step displacement estimation procedure. The first step utilizes a maximum a posteriori (MAP) estimator to determine the best integer displacement, while the second step requires solving for the regression coefficients that supply the same information as the noninteger portion of the displacement. This approach is a different and simplified procedure in that the integer displacement is measured first, and then a linear combination of only four values from the previous image, shifted by the measured integer displacement, is used. This procedure differs both from the ones which measure the displacement vector D first and interpolate the previous image, and from the ones which use only linear prediction. This method is derived and results are presented for two separate 40-frame digital image sequences. A sum of absolute error distortion measure is used to determine the optimal structure of the residual quantizer.
In 2019, the JPEG Standardization Committee initiated JPEG AI to define the first image coding specifications, taking advantage of an end-to-end learning-based coding approach. The JPEG AI specifications will be soon ...
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In 2019, the JPEG Standardization Committee initiated JPEG AI to define the first image coding specifications, taking advantage of an end-to-end learning-based coding approach. The JPEG AI specifications will be soon published as an international standard in early 2025. JPEG AI exploits the current state of the art in deep learning while taking into account considerations for a deployment in the near future. Therefore, JPEG AI design was refined in several iterations to reach the level of maturity and feasibility to both encode and decode images on mobile devices. JPEG AI offers several benefits when compared to previous conventional coding systems, namely the following: 1) superior rate-distortion performance for perceptual visual quality;2) much faster coding capability;and 3) the possibility of multipurpose optimization, such as coding for both humans and machines. JPEG AI is based on a learning-based image coding algorithm that can generate a single-stream, compact compressed domain representation, targeting both human visualization, with significant compression efficiency improvement over image coding standards, and effective performance for image processing and computer vision tasks, with the goal of supporting a royalty-free baseline. This article describes how such an objective was achieved in version 1 of the JPEG AI standard by providing technical principles behind its design while giving insights on future steps planned for future extensions of JPEG AI.
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