Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. There are several different ways in which image files can be compressed. Consider a black and white image that has a resolution of 1000*1000 and each pixel uses 8 bits to represent the intensity. So the total no of bits req= 1000*1000*8 = 80,00,000 bits per image. And consider if it is a video with 30 frames per second of the above-mentioned type images then the total bits for a video of 3 secs is: 3*(30*(8, 000, 000))=720, 000, 000 bits. As we see just to store a 3-sec video we need so many bits which is very huge. So, we need a way to have proper representation as well to store the information about the image in a minimum no of bits without losing the character of the image. Thus, image compression plays an important role.
The main objective is image compression using digital image processing. The purpose is to reduce the pixel’s intensity of an image.
Scope of project
The main contributions of this project therefore are
- Data Analysis
- Dataset Preprocessing
- Training the Model
- Testing of Dataset
Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that is able to perform processing on an image. The input of that system is a digital image and the system process that image using an efficient algorithm
- Huff man coding
- Run-length encoding
- Data loss compression
- Data loss of edge detection
- Loss inaccuracy
- Discrete cosine transform
- GLCM feature extraction
- No data loss while compression
- No data loss by edge detection
- No loss inaccuracy
- HDD: 1TB
- RAM: 8GB
MATLAB 2018 B
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