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Deep Convolutional Network based image Quality Enhancement for Low Bit Rate image Compression

Deep Convolutional Network based Image Quality Enhancement f...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Jia, Chuanmin Zhang, Xiang Zhang, Jian Wang, Shiqi Ma, Siwei Peking Univ Inst Digital Media Beijing 100871 Peoples R China Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Nanyang Technol Univ Singapore 639798 Singapore

ISBN: (纸本)9781509053162

In this contribution, a novel image quality enhancement algorithm based on convolutional network is proposed for low bit rate image compression. Specifically, a downsample procedure is performed to generate lower resolution image for low bit rate compression. While the decoder side, upsample is to be performed firstly to the original resolution. image quality is further enhanced by the proposed convolutional deep network. In particular, an optional image quality improvement network can be utilized for further enhancement after the first network. With the help of deep network, more detailed and high-frequency information can be recovered while maintaining the consistency of contour area, leading to better visual quality. Another benefit of this approach lies in that the proposed approach is fully compatible with all third-party image codec pipeline. Experimental result shows that the proposed scheme significantly outperforms JPEG in low bit rate image compression.

关键词： Low Bit Rate image Compression Deep Convolutional Network

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Predicting the Memorability of Natural-scene images

Predicting the Memorability of Natural-scene Images

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Lu, Jiaxin Xu, Mai Wang, Zulin Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Collaborat Innovat Ctr Geospatial Technol Wuhan Peoples R China

ISBN: (纸本)9781509053162

Recent work has shown that image memorability, in general, can be reliably predicted using some state-of-the-art features. However, all existing methods are not effective in predicting memorability of natural-scene images, far from human. In this paper, we propose a novel method to improve the effectiveness of memorability prediction for natural-scene images. Specifically, we argue that some of HSV colors have either positive or negative impact on memorability of natural-scene images in our Natural Scene image Memorability (NSIM) dataset. Then, we develop an HSV-based feature for memorability prediction. Finally, the HSV-based feature is combined with other efficient state-of-the-art features in our approach to predict memorability on natural scene images. Experimental results validate the effectiveness of our method.

关键词： image analysis memorability HSV

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Color image Compressive Sensing Reconstruction by Using Inter-channel Correlation

Color Image Compressive Sensing Reconstruction by Using Inte...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Chang, Kan Liang, Yun Chen, Cheng Tang, Zhenhua Qin, Tuanfa Guangxi Univ Sch Comp & Elect Informat Nanning 530004 Guangxi Peoples R China Guangxi Polytech Construct Nanning 530007 Guangxi Peoples R China

ISBN: (纸本)9781509053162

This paper proposes a novel algorithm for compressive sensing (CS) reconstruction of color images. First of all, to better describe color image characteristics, we take inter channel correlation into consideration and present two types of regularization, including inter-channel correlation-based nonlocal low-rank (ICNL) regularization and inter-channel correlation based total variation (ICTV) regularization. Afterwards, both regularization terms are incorporated into the minimization problem, and an efficient algorithm is proposed to solve the joint formulation, by using a split-Bregman-based technique. To demonstrate the effectiveness of the proposed approach, four benchmark methods are compared, and the experiments are carried out on several color images with different subrates.

关键词： compressive sensing color image inter-channel correlation nonlocal self-similarity local smoothness prior

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A Study on visual Interpretation of Network In Network

A Study on Visual Interpretation of Network In Network

引用

International Joint conference on Neural Networks

作者： Satoshi Suzuki Hayaru Shouno University of Electro-Communications

ISBN: (纸本)9781509061839

In recent years, Deep convolutional neural networks (DCNNs) have shown excellent performance in the image recognition field. A DCNN is one of the types of multi-layer neural networks, which can automatically obtain feature representation from input data. The Neocognitron, proposed by Kunihiko Fukushima in the 1980s, is a prototype of a DCNN. It was inspired by the hierarchical structure in the mammalian primary visual cortex. On the other hand, a method called Network In Network (NIN) has recently been proposed. This is a network architecture that embeds some translational symmetric micro networks in a DCNN, and it has been experimentally clarified that with it higher classification accuracy is obtained than in a conventional DCNN. However, it cannot be said that NIN has been as sufficiently analyzed from a physiological point of view compared to DCNNs. We focused on the similarities between the processing of NIN, which accumulates the feature extraction filter of a DCNN, and the operation of a mammalian visual structure called an "orientation continuity" which means preferred orientations of neighboring cells changes continuously, and pointed out the relationships between them. We also studied and pointed out the relevance of the neurophysiological knowledge and the process results obtained with high layer of NIN.

关键词： Feature extraction visualization Convolution Neurons Brain modeling Physiology Training

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A Simple but Efficient Way to Combine VLAD with Locality-constrained Linear Coding

A Simple but Efficient Way to Combine VLAD with Locality-con...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Tan, Zhenglin Wang, Wenmin Jiang, Yifeng Wang, Ronggang Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Lishui Rd 2199 Shenzhen 518055 Peoples R China

ISBN: (纸本)9781509053162

The VLAD (vector of locally aggregated descriptors) representation, derived from BoF and Fisher kernel, has shown its efficiency in the field of image search. However, assigning local descriptors to a codeword is a hard voting process, which does not consider the uncertainty and the plausibility for single codeword. In this paper, we propose an approach to combine VLAD with locality-constrained linear coding, as opposed to the original one, considering several nearest neighbors when assigning local descriptors and computing weights. In order to evaluate our proposed method, experiments are conducted on several image classification benchmarks, using VLAD for comparison. The experimental results show that our method stably outperforms VLAD in terms of classification accuracy, while producing feature representation of the same dimension without much additional computational cost.

关键词： VLAD soft-assignment image classification feature representation LLC

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Frame Rate Up-Conversion Based on Motion-Region Segmentation

Frame Rate Up-Conversion Based on Motion-Region Segmentation

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Bao, Wenbo Zhang, Xiaoyun Gao, Zhiyong Chen, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781509053162

The key problem of frame rate up-conversion (FRUC) is to obtain true motion vectors (MV), especially for the motion boundaries. In this paper, we propose a novel FRUC algorithm based on motion-region segmentation. According to region's temporal consistency, motion-regions are determined by a categorization of detected feature points' true MVs. Then, constrained by MV's spatial smoothness within a region, true motions are propagated to the entire frame. This motion-region segmentation based method achieves truthful motion vector field and preferable interpolated frames. Experiments show that comparing to the state-of-art methods, the proposed algorithm produces videos with better quality in terms of objective and subjective evaluation.

关键词： motion-region segmentation frame rate up conversion K-mean clustering feature detection vector filtering

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Joint Super-Resolution and Compression Artifact Reduction Based on Dual-Learning

Joint Super-Resolution and Compression Artifact Reduction Ba...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Lee, Oh-Young Lee, Jae-Won Lee, Dae-Yeol Kim, Jong-Ok Korea Univ Coll Elect Engn Seoul South Korea Elect & Telecommun Res Inst Dae Jeon South Korea

ISBN: (纸本)9781509053162

We propose a novel integrated framework to combine the self-learning super-resolution (SR) with dual-learning noise-reduction (NR) for compressed images. Contrary to existing learning based denoising approach, dual-learning based joint SR and NR is proposed by adding a denoised training set. It makes the proposed framework more suitable for highly compressed noise by referring to closer patch in a training set. Also, it is robust for SR artifacts since the joint framework is designed in such a way that one could learn a process to simultaneously perform NR and SR. Experimental results show that the proposed joint SR and NR framework can achieve higher objective and subjective qualities, compared with individual processing of NR and SR.

关键词： compression artifact reduction self-learning image super resolution dual-learning

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Perceptual Contrast Enhancement of Dark images Based on Textural Coefficients

Perceptual Contrast Enhancement of Dark Images Based on Text...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Chang, Yakun Jung, Cheolkon Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9781509053162

We propose perceptual contrast enhancement of dark images based on textural coefficients. The textural coefficient indicates textural degree of intensity and adaptively stretches the dynamic range in an image. First, we calculate gray level difference between a central pixel and its adjacent ones. Because some differences are obviously noticeable by human eyes, we only use unnoticeable differences to obtain the textural coefficient. We apply the just noticeable difference (JND) of the human visual system (HVS) to obtain the proper threshold. Then, we apply a Gaussian kernel to texture coefficients for avoiding excessive differences between adjacent ones. Finally, we perform optimal contrast tone mapping to obtain a mapping function. Experimental results show that the proposed method successfully enhances dark regions while avoiding over-enhancement in bright regions without halo artifact and tone distortion.

关键词： Terms Contrast enhancement dark image histogram equalization optimal contrast-tone mapping textural coefficient tone reproduction

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Joint Iterative Guidance Filtering for Compressed Depth images

Joint Iterative Guidance Filtering for Compressed Depth Imag...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Zhao, Lijun Bai, Huihui Wang, Anhong Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Taiyuan Univ Sci & Technol Inst Digital Media & Commun Taiyuan 030024 Peoples R China

ISBN: (纸本)9781509053162

In the general 3D scene, the correlation of depth image and corresponding color image exists, so many filtering methods have been proposed to improve the quality of depth images according to this correlation. Unlike the conventional methods, in this paper both depth and color information can be jointly employed to improve the quality of compressed depth image by the way of iterative guidance. Firstly, due to noises and blurring in the compressed image, a depth pre-filtering method is essential to remove artifact noises. Considering that the received geometry structure in the distorted depth image is more reliable than its color image, the color information is merged with depth image to get depth-merged color image. Then the depth image and its corresponding depth-merged color image can be used to refine the quality of the distorted depth image using joint iterative guidance filtering method. Therefore, the efficient depth structural information included in the distorted depth images are preserved relying on depth itself, while the corresponding color structural information are employed to improve the quality of depth image. We demonstrate the efficiency of the proposed filtering method by comparing objective and visual quality of the synthesized image with many existing depth filtering methods.

关键词： Joint filtering Depth image Compression Distortion Pre-filter

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Traffic At-a-Glance: Time-Bounded Analytics on Large visual Traffic Data 35

Traffic At-a-Glance: Time-Bounded Analytics on Large Visual ...

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35th IEEE Annual International conference on Computer communications (IEEE INFOCOM)

作者： Li, Xinfeng Li, Gang Yang, Fan Teng, Jin Xuan, Dong Chen, Biao Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Univ Macau Dept Comp & Informat Sci Macau Peoples R China

ISBN: (纸本)9781467399531

Massive visual traffic data have become available recently, which provides an opportunity for intelligent traffic analysis. Timely processing is particularly necessary for traffic analysis. In this paper, we study time-bounded aggregation analytics on large visual traffic data. We first find that current MapReduce framework can not work well due to two challenges: first, significant dual diversities exist on data distributions and processing time;second, no apriori knowledge on these distributions and time costs is available. However, we also observe spatial and temporal locality on data values and processing time. Based on the examination, we design TaG, an augmented MapReduce framework for time-bounded traffic analytics jobs. Particularly, we propose a novel sampling algorithm that exploits traffic data localities and stratifies samples based on data distributions and processing time. It runs in an iterative, adaptive manner without apriori knowledge. Moreover, we propose a heuristic scheduling algorithm with considerations of batch processing overhead. Further, we refine load balancing mechanism based on data processing time locality to respect job time bounds. We implement TaG on Hadoop and conduct extensive experiments on a large traffic image dataset. The evaluations on different data sizes show TaG is able to achieve high accuracy within different time bounds.

关键词： Batch data processing

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