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56th IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Gou, Aorui Sun, Heming Zeng, Xiaoyang Fan, Yibo Fudan Univ Shanghai 200433 Peoples R China Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan JST PRESTO Tokyo Japan

ISBN: (纸本)9781665451093

Traditional video coding technologies compress and reconstruct the video frames, which focus on human perception. However, video coding for machines (VCM) uses the feature stream to bridge the correlation between human perception and machine intelligence for vision tasks. We extract the features for the CU with different shapes with part of resnet architecture for VCM. However, the feature-based methods use the model to complete the forward process, which is very time-consuming for its complex architecture and parameter size. The CU architecture for the feature extraction further increases the operation times. A fast algorithm based on the Histogram of oriented gradient (HOG) is proposed for the video coding for machines with VVC intra to overcome the time-consuming problems while maintaining the performance for the vision tasks with codec. The correlation of the mode decision with the VCM performance is discussed to motivate the fast intra coding for VCM. Moreover, the VTM and VVenc are used to verify the universality of the proposed method. The proposed methods can speed up the fast encoding for 35.21% time saving with 0.26 increment for AP50 for the cityscapes dataset compared with the VTM10.0.

关键词： video coding for machines fast intra encoding versatile video coding histogram of oriented gradient semantic segmentation

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Recent Advances in video coding for machines Standard and Technologies

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ZTE Communications 2024年第1期22卷 62-76页

作者： ZHANG Qiang MEI Junjun GUAN Tao SUN Zhewen ZHANG Zixiang YU Li State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China ZTE Corporation Shenzhen 518057China Huazhong University of Science and Technology Wuhan 430074China

To improve the performance of video compression for machine vision analysis tasks,a video coding for machines(VCM)standard working group was established to promote standardization *** this paper,recent advances in video coding for machine standards are presented and comprehensive introductions to the use cases,requirements,evaluation frameworks and corresponding metrics of the VCM standard are *** the existing methods are presented,introducing the existing proposals by category and the research progress of the latest VCM ***,we give conclusions.

关键词： video coding for machines VCM video compression

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EVALUATION OF video coding for machines WITHOUT GROUND TRUTH 47

EVALUATION OF VIDEO CODING FOR MACHINES WITHOUT GROUND TRUTH

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Fischer, Kristian Hofbauer, Markus Kuhn, Christopher Steinbach, Eckehard Kaup, Andre FAU Erlangen Nurnberg Multimedia Commun & Signal Proc Erlangen Germany Tech Univ Munich Chair Media Technol Munich Germany

ISBN: (纸本)9781665405409

In the emerging field of video coding for machines, video datasets with pristine video quality and high-quality annotations are required for a comprehensive evaluation. However, existing video datasets with detailed annotations are severely limited in size and video quality. Thus, current methods have to either evaluate their codecs on still images or on already compressed data. To mitigate this problem, we propose an evaluation method based on pseudo ground-truth data from the field of semantic segmentation to the evaluation of video coding for machines. Through extensive evaluation, this paper shows that the proposed ground-truth-agnostic evaluation method results in an acceptable absolute measurement error below 0.7 percentage points on the Bjontegaard Delta Rate compared to using the true ground truth for mid-range bitrates. We evaluate on the three tasks of semantic segmentation, instance segmentation, and object detection. Lastly, we utilize the ground-truth-agnostic method to measure the coding performances of the VVC compared against HEVC on the Cityscapes sequences. This reveals that the coding position has a significant influence on the task performance.

关键词： video coding for machines VVC Semantic/Instance Segmentation Pseudo Ground Truth

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MACHINE-ATTENTION-BASED video coding for machines 30

MACHINE-ATTENTION-BASED VIDEO CODING FOR MACHINES

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30th IEEE International Conference on Image Processing (ICIP)

作者： Lee, Yegi Kim, Shin Yoon, Kyoungro Lim, Hanshin Kwak, Sangwoon Choo, Hyon-Gon Konkuk Univ Seoul South Korea Elect & Telecommun Res Inst ETRI Daejeon South Korea

ISBN: (纸本)9781728198354

Conventional video coding methods have been developed based on the human visual system (HVS). However, in recent years, video has occupied a huge portion of internet traffic, and the mount of video data for machine consumption has increased rapidly due to the progress of neural networks. This paper proposes a novel machine-attention-based video coding method for machines. Inspired by the saliency-driven research, we first extract attention regions, sensitively affecting the machine vision performance, from the object detection network. Subsequently, a maximum a posterior (MAP)-based bit allocation method is applied to assign more bits to the attention regions. Our proposed method helps to maintain high machine vision performance whereas reducing the bitrate. Experimental results show that our proposed method achieves up to 34.89% bjontegaard delta (BD)-rate reduction for the video dataset and up to 44.70% BD-rate reduction for the image dataset compared to state-of-the-art video coding technology.

关键词： video coding for machines video Compression Image Compression Machine-Attention MPEG VCM

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ON ANNOTATION-FREE OPTIMIZATION OF video coding for machines 31

ON ANNOTATION-FREE OPTIMIZATION OF VIDEO CODING FOR MACHINES

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2024 International Conference on Image Processing

作者： Windsheimer, Marc Brand, Fabian Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9798350349405;9798350349399

Today, image and video data is not only viewed by humans, but also automatically analyzed by computer vision algorithms. However, current coding standards are optimized for human perception. Emerging from this, research on video coding for machines tries to develop coding methods designed for machines as information sink. Since many of these algorithms are based on neural networks, most proposals for video coding for machines build upon neural compression. So far, optimizing the compression by applying the task loss of the analysis network, for which ground truth data is needed, is achieving the best coding performance. But ground truth data is difficult to obtain and thus an optimization without ground truth is preferred. In this paper, we present an annotation-free optimization strategy for video coding for machines. We measure the distortion by calculating the task loss of the analysis network. Therefore, the predictions on the compressed image are compared with the predictions on the original image, instead of the ground truth data. Our results show that this strategy can even outperform training with ground truth data with rate savings of up to 7.5 %. By using the non-annotated training data, the rate gains can be further increased up to 8.2 %.

关键词： video coding for machines Neural Network Compression Computer Vision Learned Image coding Machine-to-Machine Communication

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Rethinking the Joint Optimization in video coding for machines: A Case Study

Rethinking the Joint Optimization in Video Coding for Machin...

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Data Compression Conference (DCC)

作者： Gao, Changsheng Li, Zhuoyuan Li, Li Liu, Dong Wu, Feng Univ Sci & Technol China Intelligent Visual Data Coding Lab Hefei Peoples R China

ISBN: (纸本)9798350385885;9798350385878

In this work, we investigate the joint optimization strategy in the scenario of video coding for machines (VCM). We formulated two kinds of joint optimization strategies, Opt_JA and Opt_JH , and compared them with the separate optimization strategy Opt_S. The three optimization strategies are illustrated in Fig. 1. In Opt_S , we separately train the feature compression network with mean squared error (MSE). In Opt_JA , we optimize all modules jointly toward the person re-identification task. In Opt_JH , only the aggregation module and feature compression module are jointly optimized. The feature compression consists of two fully-connected (FC) layers and two batch normalization (BN) layers. Specifically, we set five compression ratios (CR): 256, 128, 64, 32, and 16. © 2024 IEEE.

关键词： Joint optimization video coding for machines

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COMPETITIVE LEARNING FOR ACHIEVING CONTENT-SPECIFIC FILTERS IN video coding for machines 31

COMPETITIVE LEARNING FOR ACHIEVING CONTENT-SPECIFIC FILTERS ...

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2024 International Conference on Image Processing

作者： Zhang, Honglei Ahonen, Jukka I. Le, Nam Yang, Ruiying Cricri, Francesco Nokia Technol Espoo Finland

ISBN: (纸本)9798350349405;9798350349399

This paper investigates the efficacy of jointly optimizing content-specific post-processing filters to adapt a human-oriented video/image codec into a codec suitable for machine vision tasks. By observing that artifacts produced by video/image codecs are content-dependent, we propose a novel training strategy based on competitive learning principles. This strategy assigns training samples to filters dynamically, in a fuzzy manner, which further optimizes the winning filter on the given sample. Inspired by simulated annealing optimization techniques, we employ a softmax function with a temperature variable as the weight allocation function to mitigate the effects of random initialization. Our evaluation, conducted on a system utilizing multiple post-processing filters within a Versatile video coding (VVC) codec framework, demonstrates the superiority of content-specific filters trained with our proposed strategies, specifically, when images are processed in blocks. Using VVC reference software VTM 12.0 as the anchor, experiments on the OpenImages dataset show an improvement in the BD-rate reduction from -41.3% and -44.6% to -42.3% and -44.7% for object detection and instance segmentation tasks, respectively, compared to independently trained filters. The statistics of the filter usage align with our hypothesis and underscore the importance of jointly optimizing filters for both content and reconstruction quality. Our findings pave the way for further improving the performance of video/image codecs.

关键词： video coding for machines post-processing filter VCM competetive learning

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REGION-OF-INTEREST-BASED video coding for machines

REGION-OF-INTEREST-BASED VIDEO CODING FOR MACHINES

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Stankiewicz, Olgierd Grajek, Tomasz Mackowiak, Slawomir Stankowski, Jakub Rozek, Slawomir Lorkiewicz, Mateusz Wawrzyniak, Maciej Domanski, Marek Poznan Univ Tech Poznan Poland

ISBN: (纸本)9798350379822;9798350379815

The paper is within the scope of video coding for machines (VCM), where video compression not only caters to human viewers but also acts as input to systems engaged in diverse machine vision tasks like object detection and tracking. A novel compression technique is proposed, which exemplifies the proficient utilization of RoIs-based video coding. It includes significant modifications to the state-of-the-art technique employing image retargeting and also addresses the related problem of resolution change. In the novel approach presented in this work, due to the proposed application of image padding, video frames encoded by an Inner Encoder (e.g. a VVC encoder) remain of constant size. Experimental evaluation demonstrates significant average bitrate reduction, up to 57% with respect to the current version of VCM, while maintaining consistent quality across various machine vision tasks and encoding scenarios.

关键词： video coding for machines VCM Regions of Interest RoI retargeting video image padding

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Privacy-preserving with Flexible Autoencoder for video coding for machines

Privacy-preserving with Flexible Autoencoder for Video Codin...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Gou, Aorui Sun, Heming Zeng, Xiaoyang Fan, Yibo Fudan Univ Acad Engn & Technol Shanghai 200437 Peoples R China Yokohama Natl Univ Fac Engn Yokohama Kanagawa 2400067 Japan

ISBN: (纸本)9798350330991;9798350331004

The dataset for video coding for machines (VCM) contains sensitive information that requires privacy preservation to address vulnerabilities. Achieving a balance to protect this sensitive data while maintaining VCM performance is crucial. We introduce an autoencoder integrated with a deep learning network that utilizes the ResNet architecture. This design blurs private details while preserving the contours, offering a high-dimensional representation that upholds privacy and VCM performance. The division position between the encoder and decoder is critical, influencing the equilibrium between compression efficacy and machine task performance. We craft a flexible, position-adjustable setting for the autoencoder to optimize this, facilitating a harmonious trade-off between bitrate and mAP across various deep-learning networks. This adaptation demonstrates superior performance relative to existing models. With FasterRCNN, our methods achieve 62.3 of mAP and 5681.29 of bitrate, and their versatility is further validated using YoloV5 and SSD.

关键词： Privacy-preserving video coding for machines versatile video coding deep learning network semantic segmentation

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Scalable video coding for Humans and machines 24

Scalable Video Coding for Humans and Machines

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IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

作者： Choi, Hyomin Bajic, Ivan, V Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (数字)9781665471893

ISBN: (纸本)9781665471893

video content is watched not only by humans, but increasingly also by machines. For example, machine learning models analyze surveillance video for security and traffic monitoring, search through YouTube videos for inappropriate content, and so on. In this paper, we propose a scalable video coding framework that supports machine vision (specifically, object detection) through its base layer bitstream and human vision via its enhancement layer bitstream. The proposed framework includes components from both conventional and Deep Neural Network (DNN)-based video coding. The results show that on object detection, the proposed framework achieves 13-19% bit savings compared to state-of-the-art video codecs, while remaining competitive in terms of MS-SSIM on the human vision task.

关键词： video compression video coding for machines

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