Learning-based image compression has reached the performance of classical methods such as BPG. One common approach is to use an autoencoder network to map the pixel information to a latent space and then approximate t...
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ISBN:
(纸本)9781728185514
Learning-based image compression has reached the performance of classical methods such as BPG. One common approach is to use an autoencoder network to map the pixel information to a latent space and then approximate the symbol probabilities in that space with a context model. During inference, the learned context model provides symbol probabilities, which are used by the entropy encoder to obtain the bitstream. Currently, the most effective context models use autoregression, but autoregression results in a very high decoding complexity due to the serialized data processing. In this work, we propose a method to parallelize the autoregressive process used for image compression. In our experiments, we achieve a decoding speed that is over 8 times faster than the standard autoregressive context model almost without compression performance reduction.
Former research on perceptual image coding was mainly developed in the traditional sequential coding frame-work, where the codestream is neither rate nor resolution scalable. In this paper, our earlier embedded subban...
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ISBN:
(纸本)0819444111
Former research on perceptual image coding was mainly developed in the traditional sequential coding frame-work, where the codestream is neither rate nor resolution scalable. In this paper, our earlier embedded subband/wavelet image coding algorithm EZBC is further developed for highly scalable image coding applications. Special attention is given to perceptual image coding under varying viewing/display conditions - a common situation in typical scalable coding application environments. Unlike the conventional perceptual image coding approach, all the perceptually coded images (individually targeted at particular viewing conditions) are decoded from a single compressed bitstream file. The experimental results show the bitrate savings by the proposed algorithm are significant, particularly for coding of high-definition (HD) images.
This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks c...
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ISBN:
(纸本)9781728185514
This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks can open up possibilities for different downstream applications. For the purpose of implementing an audio-in-image watermarking that adapts to the demands of increasingly diverse situations, a neural network architecture is designed to automatically learn the watermarking process in an unsupervised manner. In addition, a similarity network is developed to recognize the audio watermarks under distortions, therefore providing robustness to the proposed method. Experimental results have shown high fidelity and robustness of the proposed blind audio-in-image watermarking scheme.
With the emerging of the third generation (3G) wireless technology, digital media, like image and video, over wireless channel becomes more and more demanding. In this paper, the measure metrics for the wireless image...
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ISBN:
(纸本)0819444111
With the emerging of the third generation (3G) wireless technology, digital media, like image and video, over wireless channel becomes more and more demanding. In this paper, the measure metrics for the wireless image is proposed and a Qos-guarantee error control is presented, combining UEP with Forward Error Correction (FEC) and Automatic Repeat reQuest (ARQ), aiming to high quality image transmission with short delay and little energy. Simulation results show that our scheme can achieve good reconstructed image with few retransmission times and small bit budget under different channel conditions, which can reduce the energy consumed in the network interface.
With the development of deep learning, many methods on image denoising have been proposed processingimages on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excess...
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ISBN:
(纸本)9781728180687
With the development of deep learning, many methods on image denoising have been proposed processingimages on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.
Quanta image sensors are a novel paradigm in image sensor technology. Their direct application to quanta image sensors-based imaging systems is challenging because a bit-plane image is a set of binary images. In this ...
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ISBN:
(纸本)9798331529543;9798331529550
Quanta image sensors are a novel paradigm in image sensor technology. Their direct application to quanta image sensors-based imaging systems is challenging because a bit-plane image is a set of binary images. In this paper, we introduce spatiotemporal priors based on the intensity invariance and smoothness characteristics of the motion vector. Specifically, we model when the image sequences align with the correct motion vector, the spatiotemporal structure becomes more consistent. Moreover, the spatial smoothness prior is incorporated through the smoothing filtering of the evaluation metrics of motion vector candidates. The experimental results show that the proposed method is more effective than conventional methods.
An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive s...
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ISBN:
(纸本)9781728185514
An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via Gaussian models, and 3) anomaly map generation and fusion. Comparing with state-of-the-art image anomaly localization methods based on deep neural networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is among the best of several benchmarking methods.
This work proposes and implements a system to transmit in real-time slow video signals - in particular video conference signal, that is 'head and shoulder' sequences - over the public network. The proposed cod...
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ISBN:
(纸本)0819444111
This work proposes and implements a system to transmit in real-time slow video signals - in particular video conference signal, that is 'head and shoulder' sequences - over the public network. The proposed codec is based on a multiple description approach that gives N equally important and independent flows. The advantage of this codec is an intrinsic robustness to the transmission errors and to the packet loss, as the simulation results have proved. This feature results very suitable not only for IP-network, but also for every packet network like the next generation mobile systems. The approach is important also for the end user scalability since each device can decide the information to receive according to the resolution of the display and the bandwidth of the connecting link using the same source data stream.
This paper proposes Graph Grouping (GG) loss for metric learning and its application to face verification. GG loss predisposes image embeddings of the same identity to be close to each other, and those of different id...
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ISBN:
(纸本)9781728180687
This paper proposes Graph Grouping (GG) loss for metric learning and its application to face verification. GG loss predisposes image embeddings of the same identity to be close to each other, and those of different identities to be far from each other by constructing and optimizing graphs representing the relation between images. Further, to reduce the computational cost, we propose an efficient way to compute GG loss for cases where embeddings are L-2 normalized. In experiments, we demonstrate the effectiveness o(f) the proposed method for face verification on the VoxCeleb dataset. The results show that the proposed GG loss outperforms conventional losses for metric learning.
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