Influence of AVC and HEVC Compression on Detection of Vehicles Through Faster R-CNN

Chan, Pak Hung; Huggett, Anthony; Souvalioti, Georgina; Jennings, Paul; Donzella, Valentina

doi:10.1109/tits.2023.3308344

Cited by 8 publications

(2 citation statements)

References 43 publications

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“…In the realm of vehicular networks, Chan et al [110] adjusted the algorithm for autonomous vehicles' sensing data based on different compression ratios of AVC and HEVC to compare the stability of the algorithm. The study concluded that the algorithm's performance began to decline when the compression ratio reached a certain threshold.…”

Section: Hevcmentioning

confidence: 99%

A Survey on Video Streaming for Next-Generation Vehicular Networks

Huang,

Cheng,

Lien

et al. 2024

Electronics

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As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission.

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Section: Hevcmentioning

confidence: 99%

A Survey on Video Streaming for Next-Generation Vehicular Networks

Huang,

Cheng,

Lien

et al. 2024

Electronics

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“…This paper primarily discusses the latter approach, and such a scenario has led to more research on ensuring the performance of downstream task models on compressed data. One kind of method [21][22][23] is to adjust the backend model to work with compressed input, e.g., Chan et al [22] improved the performance of downstream tasks on compressed data by conducting transfer learning on backend DNNs with compressed data acquired from vehicle sensors. Another type of method involves making adjustments at the input side to minimize the impact of compression on downstream models.…”

Section: Introductionmentioning

confidence: 99%

Masked Feature Compression for Object Detection

Dai,

Song,

Jin

et al. 2024

Mathematics

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Deploying high-accuracy detection models on lightweight edge devices (e.g., drones) is challenging due to hardware constraints. To achieve satisfactory detection results, a common solution is to compress and transmit the images to a cloud server where powerful models can be used. However, the image compression process for transmission may lead to a reduction in detection accuracy. In this paper, we propose a feature compression method tailored for object detection tasks, and it can be easily integrated with existing learned image compression models. In the method, the encoding process consists of two steps. Firstly, we use a feature extractor to obtain the low-level feature, and then use a mask generator to obtain an object mask to select regions containing objects. Secondly, we use a neural network encoder to compress the masked feature. As for decoding, a neural network decoder is used to restore the compressed representation into the feature that can be directly inputted into the object detection model. The experimental results demonstrate that our method surpasses existing compression techniques. Specifically, when compared to one of the leading methods—TCM2023—our approach achieves a 25.3% reduction in compressed file size and a 6.9% increase in mAP0.5.

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