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Disparity Estimation Algorithm for Stereo video Coding Based on Edge Detecetion

Disparity Estimation Algorithm for Stereo Video Coding Based...

引用

International conference on Wireless communications and Signal processing

作者： Fan, Jingjing Liu, Feng Bao, Weibin Xia, Hongfei Nanjing Univ Posts & Telecommun Image Processing & Image Commun Jiangsu Key Lab Nanjing Peoples R China Zhengzhou Univ Technol Zhijiang Coll Hangzhou Peoples R China ZTE Telecorn Co Ltd Nanjing Peoples R China

ISBN: (纸本)9781424448562

Disparity estimation is an important technique in stereo video coding. This paper presents a disparity estimation algorithm based on edge detection. The algorithm makes full use of the human visual characteristics, that is, the human eye is more sensitive to the distortion of the edge region. Therefore, joint estimation is used for edge detection. The large code block size for coding the background region and the flat areas while small size for coding the edge region were used in this paper. Compared to the disparity estimation algorithm proposed in 181, the proposed algorithm can greatly improve the encoding speed of stereo video without affecting subjective image quality.

关键词： Stereo video Disparity Estimation Motion Estimation Global Disparity Global Motion Human Visual Characteristics

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MAPS: Joint Multimodal Attention and POS Sequence Generation for video Captioning

MAPS: Joint Multimodal Attention and POS Sequence Generation...

引用

IEEE International conference on Visual communications and image processing (VCIP) - Visual communications in the Era of AI and Limited Resources

作者： Zou, Cong Wang, Xuchen Hu, Yaosi Chen, Zhenzhong Liu, Shan Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Penn Philadelphia PA 19104 USA Wuhan Univ Wuhan Peoples R China Tencent Amer Palo Alto CA USA

ISBN: (纸本)9781728185514

video captioning is considered to be challenging due to the combination of video understanding and text generation. Recent progress in video captioning has been made mainly using methods of visual feature extraction and sequential learning. However, the syntax structure and semantic consistency of generated captions are not fully explored. Thus, in our work, we propose a novel multimodal attention based framework with Part-of-Speech (POS) sequence guidance to generate more accurate video captions. In general, the word sequence generation and POS sequence prediction are hierarchically jointly modeled in the framework. Specifically, different modalities including visual, motion, object and syntactic features are adaptively weighted and fused with the POS guided attention mechanism when computing the probability distributions of prediction words. Experimental results on two benchmark datasets, i.e. MSVD and MSR-VTT, demonstrate that the proposed method can not only fully exploit the information from video and text content, but also focus on the decisive feature modality when generating a word with a certain POS type. Thus, our approach boosts the video captioning performance as well as generating idiomatic captions.

关键词： video understanding text generation multi-modal attention sequential learning

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On the Impact of Viewing Distance on Perceived video Quality

On the Impact of Viewing Distance on Perceived Video Quality

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IEEE International conference on Visual communications and image processing (VCIP) - Visual communications in the Era of AI and Limited Resources

作者： Amirpour, Hadi Schatz, Raimund Timmerer, Christian Ghanbari, Mohammad Alpen Adria Univ Christian Doppler Lab ATHENA Klagenfurt Austria AIT Austrian Inst Technol Seibersdorf Austria Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

ISBN: (纸本)9781728185514

Due to the growing importance of optimizing the quality and efficiency of video streaming delivery, accurate assessment of user-perceived video quality becomes increasingly important. However, due to the wide range of viewing distances encountered in real-world viewing settings, the perceived video quality can vary significantly in everyday viewing situations. In this paper, we investigate and quantify the influence of viewing distance on perceived video quality. A subjective experiment was conducted with full HD sequences at three different fixed viewing distances, with each video sequence being encoded at three different quality levels. Our study results confirm that the viewing distance has a significant influence on the quality assessment. In particular, they show that an increased viewing distance generally leads to increased perceived video quality, especially at low media encoding quality levels. In this context, we also provide an estimation of potential bitrate savings that knowledge of actual viewing distance would enable in practice. Since current objective video quality metrics do not systematically take into account viewing distance, we also analyze and quantify the influence of viewing distance on the correlation between objective and subjective metrics. Our results confirm the need for distance-aware objective metrics when the accurate prediction of perceived video quality in real-world environments is required.

关键词： video streaming QoE viewing distance subjective testing

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Dictionary Learning-based Reference Picture Resampling in VVC

Dictionary Learning-based Reference Picture Resampling in VV...

引用

IEEE International conference on Visual communications and image processing (VCIP) - Visual communications in the Era of AI and Limited Resources

作者： Schneider, Jens Rohlfing, Christian Rhein Westfal TH Aachen Inst Nachrichtentech Aachen Germany

ISBN: (纸本)9781728185514

Versatile video Coding (VVC) introduces the concept of Reference Picture Resampling (RPR), which allows for a resolution change of the video during decoding, without introducing an additional Intra Random Access Point (IRAP) into the bitstream. When the resolution is increased, an upsampling operation of the reference picture is required in order to apply motion compensated prediction. Conceptually, the upsampling by linear interpolation filters fails to recover frequencies which were lost during downsampling. Yet, the quality of the upsampled reference picture is crucial to the prediction performance. In recent years, machine learning based Super-Resolution (SR) has shown to outperform conventional interpolation filters by far in regard to super-resolving a previously downsampled image. In particular, Dictionary Learning-based Super-Resolution (DLSR) was shown to improve the inter-layer prediction in SHVC [1]. Thus, this paper introduces DLSR to the prediction process in RPR. Further, the approach is experimentally evaluated by an implementation based on the VTM-9.3 reference software. The simulation results show a reduction of the instantaneous bitrate of 0.98% on average at the same objective quality in terms of PSNR. Moreover, the peak bitrate reduction is measured to 4.74% for the "Johnny" sequence of the JVET test set.

关键词： video compression reference picture resampling dictionary learning versatile video coding

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Hierarchical Reinforcement Learning Based video Semantic Coding for Segmentation

Hierarchical Reinforcement Learning Based Video Semantic Cod...

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IEEE International conference on Visual communications and image processing (VCIP)

作者： Xie, Guangqi Li, Xin Lin, Shiqi Chen, Zhibo Zhang, Li Zhang, Kai Li, Yue Univ Sci & Technol China Hefei Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9781665475921

The rapid development of intelligent tasks, e.g., segmentation, detection, and classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compression cost while maintaining the original semantic information. However, it is impractical to directly integrate the semantic metric into the traditional codecs since they cannot be optimized in an end-to-end manner. To solve this problem, some pioneering works have applied reinforcement learning to implement image-wise semantic compression. Nevertheless, the video semantic compression has not been explored since its complex reference architectures and compression modes. In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven video Semantic Coding, named as HRLVSC. Specifically, to simplify the complex mode decision of video semantic coding, we divided the action space into frame-level and CTU-level spaces in a hierarchical manner, and then explore the best mode selection for them progressively with the cooperation of framelevel and CTU-level agents. Moreover, since the modes of video semantic coding will exponentially increase with the number of frames in a Group of Pictures (GOP), we carefully investigate the effects of different mode selections for video semantic coding, and design a simple but effective mode simplification strategy for it. We have validated our HRLVSC on video segmentation task with HEVC reference software HM16.19. Extensive experimental results demonstrated that our HRLVSC can achieve over 39% BD-rate saving for video semantic coding under the Low Delay P configuration.

关键词： semantic video coding for segmentation rate-distortion optimization hierarchical reinforcement learning

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Determination of Land Regions with Segmentation in Radar video image 26

Determination of Land Regions with Segmentation in Radar Vid...

引用

26th IEEE Signal processing and communications Applications conference (SIU)

作者： Akaydin, Sumeye Nur Soysal, Gokhan Sarikaya, Tevfik Bahadir Ankara Univ Elekt Elekt Muhendisligi Bolumu Ankara Turkey ASELSAN AS REHIS Ankara Turkey

ISBN: (纸本)9781538615010

Performance increment in determination of land regions with using marine radar video frames is important in terms of target tracking, coastal security and ship route spotting. In literature, clutter map creation or sea and land statistics determination methods, by using many frames, are proposed to determine land regions. In this study, image segmentation on a single frame and also segmentation of the clutter map which is generated by using multiple frames methods have been examined in order to determination of land regions. As a result of these experiments, the performances of these methods were examined and interpreted.

关键词： radar signal processing image segmentation clutter map land determination

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Deep Local and Global Spatiotemporal Feature Aggregation for Blind video Quality Assessment

Deep Local and Global Spatiotemporal Feature Aggregation for...

引用

IEEE International conference on Visual communications and image processing (VCIP)

作者： Zhou, Wei Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China

ISBN: (纸本)9781728180687

In recent years, deep learning has achieved promising success for multimedia quality assessment, especially for image quality assessment (IQA). However, since there exist more complex temporal characteristics in videos, very little work has been done on video quality assessment (VQA) by exploiting powerful deep convolutional neural networks (DCNNs). In this paper, we propose an efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ) to predict the perceptual quality of various distorted videos in a no-reference manner. In the proposed DeepSTQ, we first extract local and global spatiotemporal features by pre-trained deep learning models without fine-tuning or training from scratch. The composited features consider distorted video frames as well as frame difference maps from both global and local views. Then, the feature aggregation is conducted by the regression model to predict the perceptual video quality. Finally, experimental results demonstrate that our proposed DeepSTQ outperforms state-of-the-art quality assessment algorithms.

关键词： Blind video quality assessment deep convolutional neural network global and local feature extraction spatiotemporal aggregation

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Quality of Experience Evaluation for Streaming video Using CGNN

Quality of Experience Evaluation for Streaming Video Using C...

引用

IEEE International conference on Visual communications and image processing (VCIP)

作者： Zhou, Zhiming Dong, Yu Song, Li Xie, Rong Li, Lin Zhou, Bing Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China MIGU Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728180687

One of the principal contradictions these days in the field of v ideo is l ying b etween t he b ooming d emand for evaluating the streaming video quality and the low precision of the Quality of Experience prediction results. In this paper, we propose Convolutional Neural Network and Gate Recurrent Unit (CGNN)-QoE, a deep learning QoE model, that can predict overall and continuous scores of video streaming services accurately in real time. We further implement state-of-the-art models on the basis of their works and compare with our method on six public available datasets. In all considered scenarios, the CGNN-QoE outperforms existing methods.

关键词： Quality of Experience (QoE) adaptive video streaming Gate Recurrent Unit

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Telemoji: A video chat with automated recognition of facial expressions

Telemoji: A video chat with automated recognition of facial ...

引用

IEEE International conference on Visual communications and image processing (VCIP) - Visual communications in the Era of AI and Limited Resources

作者： Kreinis, Alex Damri, Tom Leon, Tomer Litvak, Marina Rabaev, Irina Shamoon Coll Engn Software Engn Dept Ashdod Israel

ISBN: (纸本)9781728185514

Autism spectrum disorder (ASD) is frequently accompanied by impairment in emotional expression recognition, and therefore individuals with ASD may find it hard to interpret emotions and interact. Inspired by this fact, we developed a web-based video chat to assist people with ASD, both for real-time recognition of facial emotions and for practicing. This real-time application detects the speaker's face in a video stream and classifies the expressed emotion into one of the seven categories: neutral, surprise, happy, angry, disgust, fear, and sad. The classification is then displayed as the text label below the speaker's face. We developed this application as a part of the undergraduate project for the ***. degree in Software Engineering. Its development and testing were made with the cooperation of the local society for children and adults with autism. The application has been released for unrestricted use on https://***/. The demo is available at http://***/telemojishortdemoblur.

关键词： Facial Expressions Recognition Emotion Recognition Deep Learning Network Autism Spectrum Disorder

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Deep Motion Flow Aided Face video De-identification

Deep Motion Flow Aided Face Video De-identification

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IEEE International conference on Visual communications and image processing (VCIP) - Visual communications in the Era of AI and Limited Resources

作者： Wen, Yunqian Liu, Bo Xie, Rong Cao, Jingyi Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781728185514

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： video de-identification video anonymization privacy protection privacy-utility tradeoff

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