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检索条件"任意字段=Conference on Image and Video Communications and Processing"

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Hierarchical Reinforcement Learning Based video Semantic Coding for Segmentation

Hierarchical Reinforcement Learning Based Video Semantic Cod...

引用

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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MorphVAD: Efficient video Anomaly Detection Using Morphological Transformation

MorphVAD: Efficient Video Anomaly Detection Using Morphologi...

引用

2023 IEEE International conference on Visual communications and image processing, VCIP 2023

作者： Do, Jeonghyeok Kim, Munchurl Korea Advanced Institute of Science and Technology School of Electrical Engineering Daejeon Korea Republic of

ISBN: (纸本)9798350359855

video anomaly detection aims to identify unusual events within video sequences. Approaches that rely on reconstruction and prediction have shown impressive success in this area and continue to be actively explored. However, recent techniques often rely on pre-Trained external components such as optical flow or segmentation maps to handle the challenge of removing background that constitutes a substantial portion of video content. These additional modules, due to their computational complexity, introduce a noteworthy trade-off, particularly in real-Time video anomaly detection, where timely monitoring is paramount. So, this paper introduces an efficient video anomaly detection method (MorphVAD) that is based on a novel morphology-based masking module (Morph-Mask). The Morph-Mask harnesses the straightforward concept of morphological transformations to create masks that highlight foreground elements. So, our MorphVAD strategically incorporates these masks during the training phase, focusing on retaining only foreground-related information in memory. Through illustrative experiments and various evaluations, we demonstrate the efficiency and effectiveness of these masks detection, showing the significant enhancements in video anomaly detection performance. © 2023 IEEE.

关键词： Anomaly detection

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Enhancing video Encoding for Cloud Virtual Reality Gaming Based on User Types

Enhancing Video Encoding for Cloud Virtual Reality Gaming Ba...

引用

2023 IEEE International conference on Visual communications and image processing, VCIP 2023

作者： Song, Kai Sun, Haonan Huo, Junyan Yang, Fuzheng Yang, Kun Chen, Gaoxing Xidian University Xi'an710071 China Alibaba Group Hangzhou311121 China

ISBN: (纸本)9798350359855

Cloud Virtual Reality (VR) gaming is a novel technology that allows users to enjoy complex games on their thin clients by offloading the graphics rendering to cloud servers. The thin clients only need to perform basic decoding functions, which reduces the hardware requirements and costs. However, cloud VR gaming also faces the challenge of high bandwidth consumption when transmitting high-resolution game video streams. This paper presents a cloud VR gaming system that can transmit users' gaze point data to the server in real time to identify users' regions of interest. With this system, we verify the difference in spatial visual sensitivity caused by the different types of users. Then, a user-type-based video encoding method is proposed. Through conducting the subjective test experiment, the proposed video encoding method can reduce the bitrate for players and viewers by at least 71% and 69%, respectively, without compromising the perceptual quality. © 2023 IEEE.

关键词： Virtual reality

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Saliency-Guided Learned image Compression for Object Detection 29th

Saliency-Guided Learned Image Compression for Object Detecti...

引用

29th International conference on Neural Information processing

作者： Xiong, Haoxuan Xu, Yuanyuan Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Coll Comp & Informat Nanjing Peoples R China

ISBN: (纸本)9789819916382;9789819916399

With recent advance of computer vision techniques, an increasing amount of image and video content is consumed by machines. However, existing image and video compression schemes are mainly designed for human vision, which are not optimized concerning machine vision. In this paper, we propose a saliency guided learned image compression scheme for machines, where object detection is considered as an example task. To obtain salient regions for machine vision, a saliency map is obtained for each detected object using an existing black-box explanation of neural networks, and maps for multiple objects are merged sophistically into one. Based on a neural network-based image codec, a bitrate allocation scheme has been designed which prunes the latent representation of the image according to the saliency map. During the training of end-to-end image codec, both pixel fidelity and machine vision fidelity are used for performance evaluation, where the degradation in detection accuracy is measured without ground-truth annotation. Experimental results demonstrate that the proposed scheme can achieve up to 14.1% reduction in bitrate with the same detection accuracy compared with the baseline learned image codec.

关键词： Learned image compression Machine vision Saliency analysis Object detection

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Channel Attention Network for Wireless Capsule Endoscopy image Super-Resolution 8th

Channel Attention Network for Wireless Capsule Endoscopy Ima...

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8th International conference on Computer Vision and image processing (CVIP)

作者： Sarvaiya, Anjali Vaghela, Hiren Upla, Kishor Raja, Kiran Pedersen, Marius Sardar Vallabhbhai Natl Inst Technol SVNIT Surat India Norwegian Univ Sci & Technol NTNU Gjovik Norway

ISBN: (纸本)9783031581731;9783031581748

Wireless Capsule Endoscopy (WCE) is a technology used for examination of Gastrointestinal (GI) tract. WCE is comparatively painfree process to examine the parts of GI tract including internal walls of small intestine, stomach and esophagus. WCE uses a capsule with camera installed on board to record video while traveling through GI tract. The acquired data is transmitted wirelessly to a device outside the body. Despite numerous benefits of WCE technology, the size and battery capacity limit the quality of acquired images. One such qualitative degradation of the spatial resolution of the images which is coarser depending on the frame rate of acquired video. An effective and economic way to enhance the spatial resolution of recorded samples is by employing software-driven algorithms referred to as "Super-Resolution (SR)". Recently, Deep learning-based approaches have been used in medical-domain due to their potential to obtain qualitative High-Resolution (HR) images without the cost of additional scans. This paper presents an approach for SR of WCE images with upscaling factor x4 using deep neural network architecture which consists of a dense design of convolutional layers along with Channel Attention (CA) module to extract high-frequency details from Low-Resolution (LR) WCE images. The approach is validated on a derivative dataset of original Kvasir dataset consisting of 10, 000 samples and shows considerable improvement over the other state-of-the-art methods. Additionally, the perceptual quantitative assessment demonstrates the effectiveness of the proposed method over the others along with many distortion metrics.

关键词： Wireless Capsule Endoscopy image Super-Resolution Channel Attention

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Enhancing video Surveillance with Deep Learning-Based Real-Time Handgun Detection and Tracking 1

引用

8th International conference on Computer Vision and image processing (CVIP)

作者： Yadav, Pavinder Gupta, Nidhi Sharma, Pawan Kumar Natl Inst Technol Hamirpur Hamirpur 177005 Himachal Prades India Natl Inst Technol Kurukshetra Kurukshetra 136119 Haryana India

ISBN: (数字)9783031581816

ISBN: (纸本)9783031581809;9783031581816

Handguns, pistols, and revolvers are commonly used in today's world for committing criminal acts, requiring the need for effective surveillance and control systems. However, despite the advancement of security systems, human monitoring and involvement are still necessary to effectively combat these crimes. This paper provides a robust automated handgun identification technique for recorded videos and live CCTV footage that may be used for both control and surveillance purposes. Automatic detection of firearms is crucial for improving people's protection and safety, however, it is a challenging task because of the numerous differences in design, size, and appearance of firearms. In recent years, object detectors have improved, yielding better findings and shorter inference times. The authors used cutting-edge object detector YOLOv7 for firearm detection. A varied and demanding dataset of 15,367 images for weapon identification is also proposed, which is carefully annotated for weapon localization and classification. After analysing the data, it is determined that the model achieves an accuracy rate of 96.80% and recall rate of 90.37%.

关键词： Deep Learning Machine Learning Weapon Detection Surveillance System YOLO

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Vectorized and Optimized Dependent Quantization for Practical VVC Encoding

Vectorized and Optimized Dependent Quantization for Practica...

引用

2023 IEEE International conference on Visual communications and image processing, VCIP 2023

作者： Sainio, Joose Mercat, Alexandre Vanne, Jarno Tampere University Ultra Video Group Tampere Finland

ISBN: (纸本)9798350359855

Versatile video Coding (VVC) introduces dependent quantization (DQ) as a key coding tool, but it also has a high computational complexity. Therefore, optimizing DQ is crucial for practical VVC encoder implementations. In this paper, we propose optimization techniques for reducing the complexity of the DQ process, including efficient structure initialization, vectorized state update, rate distortion (RD) cost calculation, and last nonzero (LNZ) coefficient identification. The proposed optimizations are incorporated into the uvg266 practical VVC encoder. According to our evaluations, these optimization techniques accelerate the DQ process by over 1.7× and the entire VVC intra encoder by 1.4×, without any coding efficiency degradation. The proposed optimizations achieve more than twice the speedup over the existing techniques. © 2023 IEEE.

关键词： image coding

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Towards Deep Reference Frame in Versatile video Coding NNVC

Towards Deep Reference Frame in Versatile Video Coding NNVC

引用

2023 IEEE International conference on Visual communications and image processing, VCIP 2023

作者： Bao, Weijie Jia, Jianghao Meng, Wenhui Liu, Zizheng Xu, Xiaozhong Liu, Shan Chen, Zhenzhong Wuhan University School of Remote Sensing and Information Engineering Wuhan China Tencent Media Lab Shenzhen China Tencent Media Lab Palo AltoCA United States

ISBN: (纸本)9798350359855

In this paper, we propose a deep reference frame generation method that aims to enhance bi-direction inter prediction under random access configuration in the latest video coding standard, Versatile video Coding. Specifically, a pair of neighboring reconstructed frames are selected from decoded picture buffer and put into an optical-flow-based interpolation network to synthesize a new frame, similar to the current to-be-coded frame. Subsequently, this synthesized frame is incorporated into two-sided picture reference lists as additional reference frames. The proposed method is employed in both the encoding and decoding processes to eliminate bitstream signaling for supplementary information. The Small Ad-hoc Deep-Learning Library is utilized for implementing the proposed method. Experimental results demonstrate 3.67%/7.34%/6.51% coding efficiency improvements for Y/U/V components under the random access configuration when compared to the Versatile video Coding NNVC reference software VTM-11_NNVC-5.0. © 2023 IEEE.

关键词： video signal processing

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Multi-stage Locally and Long-range Correlated Feature Fusion for Learned In-loop Filter in VVC

Multi-stage Locally and Long-range Correlated Feature Fusion...

引用

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

作者： Kathariya, Birendra Li, Zhu Wang, Hongtao Van der Auwera, Geert Univ Missouri Kansas City Kansas City MO USA Qualcomm San Diego CA USA

ISBN: (纸本)9781665475921

Versatile video Coding (VVC)/H.266 is currently the state-of-the-art video coding standard with significant improvement in coding efficiency over its predecessor High Efficiency video Coding (HEVC)/H.265. Nonetheless, VVC is also block-based video coding technology where decoded pictures contain compression artifacts. In VVC, in-loop filters serve to suppress these compression artifacts. In this paper, convolution neural network (CNN) is utilized to better facilitate the suppression of compression artifacts over VVC. Nonetheless, our approach has uniqueness in obtaining better features by exploiting locally correlated spatial features in the pixel domain as well as long-range correlated spectral features in the discrete cosine transform (DCT) domain. In particular, we utilized CNN-features from DCT transformed input to extract high-frequency components and induce long-range correlation into the spatial CNN-features by employing multi-stage feature fusion. Our experimental result shows that the proposed approach achieves significant coding improvements up to 9.70% on average Bjontegaard Delta (BD)-Bitrate savings under AI configurations for luma (Y) components.

关键词： Versatile video Coding (VVC) In-Loop Filter Convolutional Neural Network (CNN) Receptive-Field Discrete Cosine Transform (DCT) Long Range Correlation

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Implicit Neural Representation for video Coding Through Progressive Feature Extraction

Implicit Neural Representation for Video Coding Through Prog...

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Asia-Pacific-Signal-and-Information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Lee, Jihoo Kang, Je-Won Ewha W Univ Dept Elect & Elect Engn Seoul South Korea Ewha W Univ Grad Program Smart Factory Seoul South Korea

ISBN: (纸本)9798350300673

Implicit neural representation for video (INRV) utilizes a neural network to represent a video without relying on an explicit pixel-wise representation of RGB values, and, therefore an INRV-based video coding method aims to compress a neural network and transmit its parameters through a bit-stream. In this paper, we propose a novel rate-distortion (R-D) optimized INRV-based video coding method through a progressive feature extraction module (PFEM). The PFEM consists of a series of residual blocks (RBs) that can progressively control the quality of the reconstruction, by searching the optimal network architecture. By increasing the number of RBs, the quality of a reconstruction frame is improved. However, the quality comes at the expense of overheads in transmission. In the proposed method, the number of the RBs are decided to provide the best trade-off between the frame quality and bits. Our decision model is based upon the R-D theory. Experimental results demonstrated that the proposed method provides a superior coding gain to the conventional video coding standards.

关键词： image coding

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