Data may be compressed by reducing the redundancy in the original data, but this makes the data have more errors. In this paper a novel approach of image compression based on a new method that has been created for image compression which is called Five Modulus Method (FMM). The new method consists of converting each pixel value in an 8×8 block into a multiple of 5 for each of the R, G and B arrays. After that, the new values could be divided by 5 to get new values which are 6-bit length for each pixel and it is less in storage space than the original value which is 8-bits. Also, a new protocol for compression of the new values as a stream of bits has been presented that gives the opportunity to store and transfer the new compressed image easily.
This paper proposes a novel method which combines both median filter and simple standard deviation to accomplish an excellent edge detector for image processing. First of all, a denoising process must be applied on the grey scale image using median filter to identify pixels which are likely to be contaminated by noise. The benefit of this step is to smooth the image and get rid of the noisy pixels. After that, the simple statistical standard deviation could be computed for each 22 window size. If the value of the standard deviation inside the 22 window size is greater than a predefined threshold, then the upper left pixel in the 22 window represents an edge. The visual differences between the proposed edge detector and the standard known edge detectors have been shown to support the contribution in this paper.
General TermsImage Processing and Pattern Recognition.
The standard JPEG format is almost the optimum format in image compression. The compression ratio in JPEG sometimes reaches 30:1. The compression ratio of JPEG could be increased by embedding the Five Modulus Method (FMM) into the JPEG algorithm. The novel algorithm gives twice the time as the standard JPEG algorithm or more. The novel algorithm was called FJPEG (Five-JPEG). The quality of the reconstructed image after compression is approximately approaches the JPEG. Standard test images have been used to support and implement the suggested idea in this paper and the error metrics have been computed and compared with JPEG.
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