In this paper, a novel hierarchical lossless image coding scheme using the cellular neural network (CNN) is proposed. The coding architecture of the proposed method is based on the lifting scheme that is one of the sc...
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ISBN:
(纸本)9783642175367
In this paper, a novel hierarchical lossless image coding scheme using the cellular neural network (CNN) is proposed. The coding architecture of the proposed method is based on the lifting scheme that is one of the scalable coding framework for still images, and its coding performance strongly depends on the prediction ability. To cope with this spontaneously characteristic, an image interpolation is modeled by an optimal problem that minimizes the prediction error. To achieve the high accuracy prediction with a low coding rate, two types of templates are used for dealing with the local structure of the image, and the CNN parameters are decided by the minimum coding rate learning. In the coding layer, the arithmetic coder with context modeling is used for obtaining a high coding efficiency. Experimental results in various standard test images suggest that the coding performance of our proposed method is better than that of conventional hierarchical coding schemes.
A design approach is presented which allows the optimization of coefficients for symmetric and separable finite impulse response (FIR) interpolation filters for multiresolution coding schemes, The interpolation filter...
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A design approach is presented which allows the optimization of coefficients for symmetric and separable finite impulse response (FIR) interpolation filters for multiresolution coding schemes, The interpolation filters serve as optimal inverse filters in the Wiener sense and are designed to match the characteristics of the specific filters used for decimation as well as for the statistics of ''typical'' images to be reconstructed, Applied to the coding of test images in a four-level progressive pyramid scheme, the optimal interpolation filters generated substantially improved rate-distortion results compared to conventional filters.
We propose an embedded, block-based, image wavelet transform coding algorithm of low complexity. It uses a recursive set-partitioning procedure to sort subsets of wavelet coefficients by maximum magnitude with respect...
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We propose an embedded, block-based, image wavelet transform coding algorithm of low complexity. It uses a recursive set-partitioning procedure to sort subsets of wavelet coefficients by maximum magnitude with respect to thresholds that are integer powers of two. It exploits two fundamental characteristics of an image transform-the well-defined hierarchical structure, and energy clustering in frequency and in space. The two partition strategies allow for versatile and efficient coding of several image transform structures, including dyadic, blocks inside subbands, wavelet packets, and discrete cosine transform (DCT). We describe the use of this coding algorithm in several implementations, including reversible (lossless) coding and its adaptation for color images, and show extensive comparisons with other state-of-the-art coders, such as set partitioning in hierarchical trees (SPIHT) and JPEG2000. We conclude that this algorithm, in addition to being very flexible, retains all the desirable features of these algorithms and is highly competitive to them in compression efficiency.
This work studies problems of source and joint source-channel coding under the requirement that the encoder can produce an exact copy of the compressed source constructed by the decoder. This requirement, termed here ...
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This work studies problems of source and joint source-channel coding under the requirement that the encoder can produce an exact copy of the compressed source constructed by the decoder. This requirement, termed here as the common reconstruction constraint (CR), is satisfied automatically in rate-distortion theory for single sources. However, in the common formulation of problems of lossy source coding with side information at the decoder (the Wyner-Ziv problem), distributed source coding, and joint source-channel coding for networks, the destination can exploit the information it receives in a manner that cannot be exactly reproduced at the sender side. Some applications, like the transmission of sensitive medical information, may require that both sides-the sender and the receiver-will share a common version of the compressed data, for the purpose of future discussions or consulting. The purpose of this work is to study the implications of CR constraints on the achievable rates in scenarios of lossy source coding and lossy transmission of sources. Three problems are examined: source coding with side information at the decoder, simultaneous transmission of data and state over state-dependent channels, and joint source-channel coding for the degraded broadcast channel. Single-letter characterizations of the optimal performance are developed for these problems, under corresponding CR constraints. Implications of this constraint on problems of joint source-channel coding in networks are discussed.
Channel coding for single-user channels with rate-limited, coded, partial channel state information at the transmitter and full side information (SI) at the receiver is studied. In the first part of the current work, ...
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Channel coding for single-user channels with rate-limited, coded, partial channel state information at the transmitter and full side information (SI) at the receiver is studied. In the first part of the current work, we consider joint source-channel state channel coding. In particular, we deal with lossy transmission of a source, over a cost-constrained state controlled channel where the receiver gets full SI and the transmitter receive coded partial SI. We derive a single-letter characterization of the achievable distortion-cost triples. From this characterization, a separation principle follows for both, coding of the main source and the transmitter SI. In the second part, we consider channel coding when the transmitter receives multiple partial, rate-limited descriptions of the state, and the receiver gets full SI. Two rate-limited descriptions of the state sequence are generated and conveyed to the transmitter, where each description can be lost independently during this transmission. For three different possible partial SI at the transmitter, we explore a channel coding strategy with three different forward channel rates. Inner and outer bounds are derived on the set of achievable partial description and forward channel rates. Furthermore, special cases where the inner bound is tight are studied. Similarities between coding of SI as multiple partial descriptions and the multiple description problem of source coding theory are pointed out.
The text deals with a hierarchical gray level image coding algorithm applying subsampling and temporal progressive resolution. Data compression is attained by predictive coding of the samples, quantization, image prep...
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The text deals with a hierarchical gray level image coding algorithm applying subsampling and temporal progressive resolution. Data compression is attained by predictive coding of the samples, quantization, image preprocessing and extensive reduction of coded samples. Es wird ein hierarchisches Verfahren zur Kodierung von Grauwertbildern beschrieben, das eine Unterabtastung des Bildes vornimmt und im Verlaufe des Kodiervorganges die volle Auflösung des Bildes erreicht. Eine Datenkompression wird durch die Prädiktionskodierung der Stützstellen, durch Quantisierung des Prädiktions-Fehlers, durch Bildvorverarbeitung und durch eine weitgehende Reduktion der kodierten Stützstellen realisiert. Ce texte présente un algorithme hiérarchique de codage d'images à niveaux de gris appliquant le sous-échantillonnage et la résolution temporelle progressive. La compression de données est obtenue par le codage prédictif des échantillons, par la quantification, le prétraitement et la réduction massive des échantillons codés.
We consider the coding of featured contours, i.e. texture, object or motion boundaries in images. The MPEG-4 and MPEG-7 video coding standards provide for a content-based representation of video information, so effici...
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We consider the coding of featured contours, i.e. texture, object or motion boundaries in images. The MPEG-4 and MPEG-7 video coding standards provide for a content-based representation of video information, so efficient coding of boundaries can play an important role. We propose a new coding scheme that uses an asymmetric binary tree. We show that the new scheme outperforms conventional quadtree, binary tree, contour and READ coding algorithms applied to typical boundary images and also offers competitive performance for general-purpose two-level image coding. (C) 2003 Elsevier B.V. All rights reserved.
Optical camera communication (OCC) has attracted increasing attention recently thanks to the wide usage of LED and high-resolution cameras. The lens-image sensor structure enables the camera distinguish light from var...
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ISBN:
(纸本)9781665458221
Optical camera communication (OCC) has attracted increasing attention recently thanks to the wide usage of LED and high-resolution cameras. The lens-image sensor structure enables the camera distinguish light from various source, which is ideal for spatial MIMO. Hence, OCC can be applied to several emerging application scenarios, such as vehicle and drone communications. However, distance is a major bottleneck for OCC system, because the increase in distance makes it difficult for the camera to distinguish adjacent LEDs, which we call LED spatial mixing. In this paper, we propose a novel hierarchical coding scheme named as ONIONCODE to support dynamic range of channel capacity in one-to-many OCC scenario. ONIONCODE adopts a multi-priority receiving scheme, i.e., the receivers can dynamically discard the low-priority bit streams according to the measured channel capacity. ONIONCODE achieves this based on a key insight that, the luminance level of a mix-LED is distinguishable. We prototype a LED-based OCC system to evaluate the efficacy of ONIONCODE and the results show that ONIONCODE achieves a higher coding efficiency and overall throughput compared with the existing hierarchical coding.
This work studies the multiple-access channel (MAC) controlled by random parameters, with full side information at the decoder, and partial, rate limited, side information (SI) at the encoders. A single-letter charact...
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This work studies the multiple-access channel (MAC) controlled by random parameters, with full side information at the decoder, and partial, rate limited, side information (SI) at the encoders. A single-letter characterization of the capacity region is derived, for the special case where the SI is degraded. Here degraded SI refers to the case where the SI available at one of the encoders is a subset of the SI available at the other encoder. Inner and outer bounds are derived on the capacity region of that channel for the general case where there are no restrictions on the structure of the SI at the two encoders. The techniques employed for coding the rate-limited SI, and the achievable regions so obtained, are closely related to the problems of hierarchical source coding, and multiple descriptions.
Light field (LF) has become an attractive representation of immersive multimedia content for simultaneously capturing both the spatial and angular information of the light rays. In this paper, we present a LF image co...
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Light field (LF) has become an attractive representation of immersive multimedia content for simultaneously capturing both the spatial and angular information of the light rays. In this paper, we present a LF image compression framework driven by a generative adversarial network (GAN)-based sub-aperture image (SAI) generation and a cascaded hierarchical coding structure. Specifically, we sparsely sample the SAIs in LF and propose the GAN of LF (LF-GAN) to generate the unsampled SAIs by analogy with adversarial learning conditioned on its surrounding contexts. In particular, the LF-GAN learns to interpret both the angular and spatial context of the LF structure and, meanwhile, generates intermediate hypothesis for the unsampled SAIs in a certain position. Subsequently, the sampled SAIs and the residues of the generated-unsampled SAIs are re-organized as pseudo-sequences and compressed by standard video codecs. Finally, the hierarchical coding structure is adopted for the sampled SAI to effectively remove the inter-view redundancies. During the training process of IF-GAN, the pixel-wise Euclidean loss and the adversarial lass are chosen as the optimization objective, such that sharp textures with less blurring in details can be produced. Extensive experimental results show that the proposed LF-GAN-based LF image compression framework outperforms the state-of-the-art learning-based IF image compression approach with on average 4.9% BD-rate reductions over multiple LF datasets.
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