Optimized Data Compression through Effective Analysis of JPEG Standard

Bharadwaj, N. Akash; Rao, C. Srinivasa; Rahul, *; Gururaj, C.

doi:10.1109/esci50559.2021.9396904

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…This approach results in an excessive data load and processing burden on these units. Currently, JPEG image compression technology is recognized as one of the most prevalent image compression methods globally, due to its significant capacity to reduce file sizes [8]. Therefore, this paper integrates MEC for image data processing, simultaneously ensuring accuracy in target detection.…”

Section: Introductionmentioning

confidence: 99%

Optimization of User Service Rate with Image Compression in Edge Computing-Based Vehicular Networks

Zhang,

Li,

Guan

et al. 2024

Mathematics

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The prevalence of intelligent transportation systems in alleviating traffic congestion and reducing the number of traffic accidents has risen in recent years owing to the rapid advancement of information and communication technology (ICT). Nevertheless, the increase in Internet of Vehicles (IoV) users has led to massive data transmission, resulting in significant delays and network instability during vehicle operation due to limited bandwidth resources. This poses serious security risks to the traffic system and endangers the safety of IoV users. To alleviate the computational load on the core network and provide more timely, effective, and secure data services to proximate users, this paper proposes the deployment of edge servers utilizing edge computing technologies. The massive image data of users are processed using an image compression algorithm, revealing a positive correlation between the compression quality factor and the image’s spatial occupancy. A performance analysis model for the ADHOC MAC (ADHOC Medium Access Control) protocol is established, elucidating a positive correlation between the frame length and the number of service users, and a negative correlation between the service user rate and the compression quality factor. The optimal service user rate, within the constraints of compression that does not compromise detection accuracy, is determined by using the target detection result as a criterion for effective compression. The simulation results demonstrate that the proposed scheme satisfies the object detection accuracy requirements in the IoV context. It enables the number of successfully connected users to approach the total user count, and increases the service rate by up to 34%, thereby enhancing driving safety, stability, and efficiency.

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Section: Introductionmentioning

confidence: 99%

Optimization of User Service Rate with Image Compression in Edge Computing-Based Vehicular Networks

Zhang,

Li,

Guan

et al. 2024

Mathematics

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“…Nowadays, to compress images and achieving promising compression performance, deep learning approaches [5], [6] are adopted in image compression fields. To reduce the file size but keeping the file format, a quantization table change method was proposed in [7] to further recompress JPEG images. But there is no doubt that the image clarity was inevitably reduced with large quantization coefficients.…”

Section: Introductionmentioning

confidence: 99%

A CABAC pre-coding based and lossless recompression method for JPEG images

Jiang¹,

Lai²,

Sheng³

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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As the number of application of images on the Internet increases, how to store and transmit these images becomes a big challenge. JPEG as the most widely used image compression format on the Internet is often applied to pictures compression. However, just using JPEG alone to compress images is not enough now. In hence, some methods use improved entropy coding to further recompress JPEG images losslessly or process the images on DCT domain for lossy recompression. These methods are useful and work for various images. But there is no special design for fixed surveillance applications. Depending on the feature of images generated by a same fixed surveillance camera, a JPEG image lossless recompression method based on CABAC pre-coding, residual coefficients between JPEG image group and simplified context prediction is proposed by us. With a little reduction of decoding time as well as little increase of encoding time, average 27% bits saving can be achieved in the experiment.

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