Lossy Image Compression by Rounding the Intensity Followed by Dividing (RIFD)

Abstract

Several millions of digital images are transmitted every minute via mobile applications. The main feature of these images is their huge sizes. However, most of their details are not important such as natural images. Continuous efforts are achieved to utilize the wireless bandwidth and capacity for mobile users. One of the most significant efforts is the image compression. The aim of this study is to introduce a new lossy technique called RFID for compressing images, in order to overcome these problems by achieving high compression ratio. The proposed technique depends on increasing the redundancy and similarity among the neighboring pixels of images by rounding the pixels' intensities followed by the dividing process, which makes compression feasible. It can be applied alone or followed by any lossless compression algorithm. Experimental results show a great performance when RIFD followed by Huffman algorithm.

Enhancing Quality of Lossy Compressed Images using Minimum Decreasing Technique

“…The researchers in [4] indicate that data can be compressed by decreasing the redundancy in the main data, but this makes the data have further errors. In this study a novel method of an image compression depend on a different technique which has been formed for image compression which has been called Five Modulus Method (FMM).…”

“…Otair and Shehadeh [4] proposed a novel lossy image compression technique called RIFD for compressing images. This scheme leans on increasing the redundancy and resemblance among the close pixels of images by rounding the pixels' intensities followed by the dividing process, which makes compression attainable, the main idea of the RIFD algorithm is based on two facts: 1) adjacent pixels are correlated or so identical; 2) the human sight can perceive a very limited number of intensities.…”