Abstract
Background: In recent times, transmission of information over a wireless channel is increasing at an exponential rate. Applications based on IOT will exceed all form of data available over the internet, it can be in the form of data based on videos and images which alone size up to 70% of the global traffic.
 Methodology: In this paper, an encoding technique based on discrete cosine transform (DCT) have been utilized to reduce information of a CT image that is unwanted or a human eye is unable to perceive. For medical images, preserving maximum information in order to properly diagnose any disease is important and for such application, the amount of quantization needed to gain an image with minimum error is to observe the Correlation index (CI).
 Results: CI at 50% Quality Quantization Table (QQT) for image 1 was found to be 0.9981 and its size reduced to 0.403 MB, it means that the compressed image is 99.8% similar to the original image. But its similarity reduces with the increase in QQT, its size will also decrease by at this point quality will degrade too.
 Conclusion: In order to store images on the drive, it is imperative to apply a compression technique. In this paper, a compression technique is proposed utilizing a DCT algorithm to reduce an image size with different QQT provided for quality assessment. Each image has a different set of information and to cater their differences this QQT can be manipulated for best possible quality with acceptable size on disk.
Highlights
Huge amount of information is transferred or uploaded from different sensory sources including cameras, sensors or any other equipment
It is required to keep the record of the CT scan for about 10 years, a huge amount of storage capacity is required but this can be avoided if the size of those images were to reduce by applying efficient compression algorithm without compromising its overall quality[3]
Correlation index at 50% quality quantization table (QQT) was 0.9981, which points the fact that the original image and compressed image at 50% (QQT) has a similarity index of 99.81%
Summary
Huge amount of information is transferred or uploaded from different sensory sources including cameras, sensors or any other equipment. If the data needs to be transmitted to a server location containing a data hub for multiple user interfaces, such a process can be considered to be IOT1. For medical applications such as CT images, produced a bulk amount of information that needs to be stored and transmitted at a very high rate. Results: CI at 50% Quality Quantization Table (QQT) for image 1 was found to be 0.9981 and its size reduced to 0.403 MB, it means that the compressed image is 99.8% similar to the original image. A compression technique is proposed utilizing a DCT algorithm to reduce an image size with different QQT provided for quality assessment. Each image has a different set of information and to cater their differences this QQT can be manipulated for best possible quality with acceptable size on disk
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