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Optimization of Probability Distributions for Residual Coding of Screen Content

Optimization of Probability Distributions for Residual Codin...

引用

IEEE visual communications and image processing (VCIP)

作者： Hannah Och Tilo Strutz André Kaup Friedrich-Alexander University Erlangen-Nürnberg (FAU) Multimedia Communications and Signal Processing Erlangen Germany Deutsche Telekom AG Leipzig University of Telecommunications Institute of Communications Engineering Leipzig Germany

ISBN: (纸本)9781728173221

Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9 % on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11 % and 18 % rate, respectively.

关键词： Adaptation models image coding image color analysis visual communication Bit rate Probability distribution Optimization

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Progressive Co-Attention Network for Fine-Grained visual Classification

Progressive Co-Attention Network for Fine-Grained Visual Cla...

引用

IEEE visual communications and image processing (VCIP)

作者： Tian Zhang Dongliang Chang Zhanyu Ma Jun Guo Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9781728173221

Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle variations among highly-confused categories. Most existing methods only take an individual image as input, which may limit the ability of models to recognize contrastive clues from different images. In this paper, we propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise similarity by encouraging interaction between the feature channels within same-category image pairs to capture the common discriminative features. Considering that complementary information is also crucial for recognition, we erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminative regions. The proposed model has achieved competitive results on three fine-grained visual classification benchmark datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft.

关键词： visualization image recognition visual communication Atmospheric modeling Force Benchmark testing Automobiles

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An Error Self-learning Semi-supervised Method for No-reference image Quality Assessment

An Error Self-learning Semi-supervised Method for No-referen...

引用

IEEE visual communications and image processing (VCIP)

作者： Yingjie Feng Sumei Li Sihan Hao School of Electrical and Information Engineering Tianjin University TianJin China

ISBN: (纸本)9781728173221

In recent years, deep learning has achieved significant progress in many respects. However, unlike other research fields with millions of labeled data such as image recognition, only several thousand labeled images are available in image quality assessment (IQA) field for deep learning, which heavily hinders the development and application for IQA. To tackle this problem, in this paper, we proposed an error self-learning semi-supervised method for no-reference (NR) IQA (ESSIQA), which is based on deep learning. We employed an advanced full reference (FR) IQA method to expand databases and supervise the training of network. In addition, the network outputs of expanding images were used as proxy labels replacing errors between subjective scores and objective scores to achieve error self-learning. Two weights of error back propagation were designed to reduce the impact of inaccurate outputs. The experimental results show that the proposed method yielded comparative effect.

关键词： image quality Deep learning Training Backpropagation image recognition visual communication Databases

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Deep Motion Flow Aided Face Video De-identification

Deep Motion Flow Aided Face Video De-identification

引用

IEEE visual communications and image processing (VCIP)

作者： Yunqian Wen Bo Liu Rong Xie Jingyi Cao Li Song Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science University of Technology Sydney Sydney Australia

ISBN: (纸本)9781728173221

Advances in cameras and web technology have made it easy to capture and share large amounts of face videos over to an unknown audience with uncontrollable purposes. These raise increasing concerns about unwanted identity-relevant computer vision devices invading the characters's privacy. Previous de-identification methods rely on designing novel neural networks and processing face videos frame by frame, which ignore the data feature in redundancy and continuity. Besides, these techniques are incapable of well-balancing privacy and utility, and per-frame evaluation is easy to cause flicker. In this paper, we present deep motion flow, which can create remarkable de-identified face videos with a good privacy-utility tradeoff. It calculates the relative dense motion flow between every two adjacent original frames and runs the high quality image anonymization only on the first frame. The de-identified video will be obtained based on the anonymous first frame via the relative dense motion flow. Extensive experiments demonstrate the effectiveness of our proposed de-identification method.

关键词： Privacy Computer vision visual communication image processing Redundancy Neural networks Cameras

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The Effect of SAR Speckle Removal in SAR-Optical image Fusion

The Effect of SAR Speckle Removal in SAR-Optical Image Fusio...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Semih Genç ay Caner Ö zcan Bilgisayar Teknolojileri Manisa Celal Bayar &#x00DC niversitesi Manisa T&#x00FC rkiye Yaz&#x0131 l&#x0131 m M&#x00FC hendisli&#x011F i Karab&#x00FC k &#x00DC niversitesi Karab&#x00FC k T&#x00FC

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

Due to the imaging mechanism of Synthetic Aperture Radar (SAR) and the noise in the images, visual identification of objects in the scene is not as easy as in optical images. SAR images have limited color information and cannot reflect the spectral information of objects. Optical images, on the other hand, have rich spectral information. SAR-Optical image fusion is an important area of study so that SAR data can be easily evaluated by anyone, but it is difficult to find a matching SAR and optical image of the same scene. In order to overcome this difficulty, Sentinel-1 and Sentinel-2 datasets have been published and image fusion studies have been carried out with various methods. However, it has been observed that the effect of SAR noise removal before merging on image fusion methods has not been investigated. In the studies conducted to investigate this effect, five different fusion algorithms used in the literature were tested with twenty different image groups using different noise reduction ratios. The success of the fusion results obtained was compared with five different metrics that are widely used in the literature. The images and metric results obtained as a result of the tests showed that the removal of speckle noise in the SAR data has a positive effect on the fusion results.

关键词： Measurement visualization Speckle Optical imaging Adaptive optics Optical sensors Optical reflection

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Learn A Compression for Objection Detection - VAE with a Bridge

Learn A Compression for Objection Detection - VAE with a Bri...

引用

IEEE visual communications and image processing (VCIP)

作者： Yixin Mei Fan Li Li Li Zhu Li School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China University of Science and Technology of China Hefei China University of Missouri-Kansas City Kansas City MO USA

ISBN: (纸本)9781728173221

Recent advances in sensor technology and wide deployment of visual sensors lead to a new application whereas compression of images are not mainly for pixel recovery for human consumption, instead it is for communication to cloud side machine vision tasks like classification, identification, detection and tracking. This opens up new research dimensions for a learning based compression that directly optimizes loss function in vision tasks, and therefore achieves better compression performance vis-a-vis the pixel recovery and then performing vision tasks computing. In this work, we developed a learning based compression scheme that learns a compact feature representation and appropriate bitstreams for the task of visual object detection. Variational Auto-Encoder (VAE) framework is adopted for learning a compact representation, while a bridge network is trained to drive the detection loss function. Simulation results demonstrate that this approach is achieving a new state-of-the-art in task driven compression efficiency, compared with pixel recovery approaches, including both learning based and handcrafted solutions.

关键词： Bridges Training visualization image coding visual communication Transforms Object detection

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Analyzing Time Complexity of Practical Learned image Compression Models

Analyzing Time Complexity of Practical Learned Image Compres...

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IEEE visual communications and image processing (VCIP)

作者： Xiaohan Pan Zongyu Guo Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

We have witnessed the rapid development of learned image compression (LIC). The latest LIC models have outperformed almost all traditional image compression standards in terms of rate-distortion (RD) performance. However, the time complexity of LIC model is still underdiscovered, limiting the practical applications in industry. Even with the acceleration of GPU, LIC models still struggle with long coding time, especially on the decoder side. In this paper, we analyze and test a few prevailing and representative LIC models, and compare their complexity with traditional codecs including H.265/HEVC intra and H.266/VVC intra. We provide a comprehensive analysis on every module in the LIC models, and investigate how bitrate changes affect coding time. We observe that the time complexity bottleneck mainly exists in entropy coding and context modelling. Although this paper pay more attention to experimental statistics, our analysis reveals some insights for further acceleration of LIC model, such as model modification for parallel computing, model pruning and a more parallel context model.

关键词： Analytical models image coding Codecs visual communication Computational modeling Rate-distortion Decoding

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Deep Learning-Based Blind image Super-Resolution using Iterative Networks

Deep Learning-Based Blind Image Super-Resolution using Itera...

引用

IEEE visual communications and image processing (VCIP)

作者： Asfand Yaar Hasan F. Ates Bahadir K. Gunturk School of Engineering and Natural Sciences Istanbul Medipol University Turkey

ISBN: (纸本)9781728173221

Deep learning-based single image super-resolution (SR) consistently shows superior performance compared to the traditional SR methods. However, most of these methods assume that the blur kernel used to generate the low-resolution (LR) image is known and fixed (e.g. bicubic). Since blur kernels involved in real-life scenarios are complex and unknown, per-formance of these SR methods is greatly reduced for real blurry images. Reconstruction of high-resolution (HR) images from randomly blurred and noisy LR images remains a challenging task. Typical blind SR approaches involve two sequential stages: i) kernel estimation; ii) SR image reconstruction based on estimated kernel. However, due to the ill-posed nature of this problem, an iterative refinement could be beneficial for both kernel and SR image estimate. With this observation, in this paper, we propose an image SR method based on deep learning with iterative kernel estimation and image reconstruction. Simulation results show that the proposed method outperforms state-of-the-art in blind image SR and produces visually superior results as well.

关键词： Deep learning visual communication Simulation Superresolution Estimation Noise measurement Iterative methods

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Mixed-precision Quantization with Dynamical Hessian Matrix for Object Detection Network

Mixed-precision Quantization with Dynamical Hessian Matrix f...

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IEEE visual communications and image processing (VCIP)

作者： Zerui Yang Wen Fei Wenrui Dai Chenglin Li Junni Zou Hongkai Xiong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781728173221

Mixed-precision quantization with adaptive bitwidth allocation for neural network has achieved higher compression rate and accuracy in classification task. However, it has not been well explored for object detection networks. In this paper, we propose a novel mixed-precision quantization scheme with dynamical Hessian matrix for object detection networks. We iteratively select a layer with the lowest sensitivity based on the Hessian matrix and downgrade its precision to reach the required compression ratio. The L-BFGS algorithm is utilized for updating the Hessian matrix in each quantization iteration. Moreover, we specifically design the loss function for objection detection networks by jointly considering the quantization effects on classification and regression loss. Experimental results on RetinaNet and Faster R-CNN show that the proposed DHMQ achieves state-of-the-art performance for quantized object detec-tors.

关键词： Quantization (signal) image coding Sensitivity visual communication Heuristic algorithms Object detection Detectors

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See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recovery from a Single Rainy image

See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recove...

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IEEE visual communications and image processing (VCIP)

作者： Ping Wang Wei Wu Zhu Li Yong Liu State Key Laboratory of Integrated Services Networks Xidian University Xi'an China University of Missouri Kansas City USA

ISBN: (纸本)9781728173221

Scale-Invariant Feature Transform (SIFT) is one of the most well-known image matching methods, which has been widely applied in various visual fields. Because of the adoption of a difference of Gaussian (DoG) pyramid and Gaussian gradient information for extrema detection and description, respectively, SIFT achieves accurate key points and thus has shown excellent matching results but except under adverse weather conditions like rain. To address the issue, in the paper we propose a divide-and-conquer SIFT key points recovery algorithm from a single rainy image. In the proposed algorithm, we do not aim to improve quality for a derained image, but divide the key point recovery problem from a rainy image into two sub-problems, one being how to recover the DoG pyramid for the derained image and the other being how to recover the gradients of derained Gaussian images at multiple scales. We also propose two separate deep learning networks with different losses and structures to recover them, respectively. This divide-and-conquer scheme to set different objectives for SIFT extrema detection and description leads to very robust performance. Experimental results show that our proposed algorithm achieves state-of-the-art performances on widely used image datasets in both quantitative and qualitative tests.

关键词： Deep learning visualization Rain visual communication image processing image matching Dogs

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