Abstract
In recent times, transmission of information over wireless channels is increasing at an exponential rate. Internet is major source of information; it can be in the form of video or images which alone size up to 72% of the global traffic. In order to tackle such immense data, available channel may not be enough for transmission or reception, in this regard it is imperative to use efficient compressions techniques to reduce its size. Compressed image quality depends on the Quantization Table performed after spatial transformation like Discrete Cosine Transform. Size of a raw image captured by Digital Single-Lens Reflex, having all of its traits can easily exceed 20 Mega Bytes. In proposed compression algorithm, a Crisp parameter p modification step is introduced for effective compression of an image by utilizing standard Joint Picture Expert Group Quantization Table as a baseline model. After implementation of the proposed algorithm, Mean Opinion Score is obtained from the masses through an online survey and it provide the scores of 47.633 at p = 1, 62.74 at p = 8, and 83.252 at p = 16. According to Mean opinion score, best trade-off between quality and size of an image is between the values of p ranges from 11-20, this is also proved by Mean Squared Error and Peak Signal to Noise Ratio, as their ranges are 0.00038-0.000301 and 34.09-34.92 dB respectively. Compression Ratio which is from 6.49-5.76 is also acceptable for the given range.
Highlights
Huge amount of information is transferred or uploaded from different sensory sources including cameras, sensors or any other equipment
This range is selected after visualizing simulation results on MATLAB, it is possible to accept the value of p less than zero but that reconstructed image quality will be of no use, we can take value greater than 100, and in that case there will be no compression as QT values will be close to unity
The mean was calculated from the acquired percentages of all three images to determine the cumulative MOS, the reconstructed image at p = 1 was found to be 47.633%, which means that the image at p = 1 has the least resemblance with the original image and on the other hand, the image at p =16 has the most resemblance as its cumulative score is more than 83%
Summary
Huge amount of information is transferred or uploaded from different sensory sources including cameras, sensors or any other equipment. Almost all users utilize their gadgets like cell phones, digital cameras or tablets to captures images and videos which usually stacks up to over 80% of their storage capacity. We can store these images in the cloud from where we can retrieve them when needed. It is imperative to compress these images or videos for fast downloading and uploading. There are some hurdles that occur during the transmission such as lack of available bandwidth, not enough storage capacity and downloading speed in case of retrieving information from internet. Compression of an image basically exploits the ability of human eye which is sensitive to
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