A New Approach to Image Compression in Industrial Internet of Things
Nahid Hajizadeh, Pirooz Shamsinejad, Reza Javidan

TL;DR
This paper introduces a novel adaptive lossy image compression method for IIoT sensor images using K-Means++ and IEC, achieving higher quality compression without increasing bandwidth.
Contribution
The study presents the first application of a reusable, color-based clustering technique combining K-Means++ and IEC for improved image compression in IIoT sensor networks.
Findings
Higher image quality at the same compression rate compared to existing clustering methods.
Effective compression of greenhouse sensor images with improved efficiency.
Demonstrated success on real-world dataset from a smart greenhouse.
Abstract
Applying image sensors in automation of Industrial Internet of Things (IIoT) technology is on the rise, day by day. In such companies, a large number of high volume images are transmitted at any moment; therefore, a significant challenge is reducing the amount of transmitted information and consequently bandwidth without reducing the quality of images. Image compression in sensors, in this regard, will save bandwidth and speed up data transmitting. There are several pieces of research in image compression for sensor networks, but, according to the nature of image transfer in IIoT, there is no study in this particular field. In this paper, it is for the first time that a new reusable technique to improve productivity in image compression is introduced and applied. To do this, a new adaptive lossy compression technique to compact sensor-generated images in IIoT by using K- Means++ and…
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Taxonomy
TopicsAdvanced Data Compression Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
