Optimized Image Compression Techniques for the Embedded Processors

Abstract

Optimizing the utilization factor of the system resources such as efficiency, bandwidth, and the storage capacity for cost reduction is one important aim of enormous amount of studies. For the image compression one can use the embedded processors as the most suitable ones. This image compression schemes for images will be based on the Discrete Cosine Transform (DCT). This paper implements an efficient and effective algorithm for still image compression of relatively high signal to noise ratio. The implemented technique considers that only zeros of the zigzag scanning is the repeated runs. This results in possibility of zero byte of the Run Length Encoding (RLE) output elimination.Word-length reduction and higher compression ratio can be customized.

Embedded Image Compression: A Review

“…Lossless compression technique involves no loss of information. If data have been lossless compressed the original data can be recovered exactly from the compressed data, Lossless compression is generally used for applications that cannot tolerate any difference between the original and reconstructed data [8].…”

“…Near lossless compression technique is a lossy compression method where the reconstructed pixels differ from the original pixels by no more than a predetermined value, in near-lossless compression data is guaranteed to be within a specified range based on the near-lossless threshold [8].…”

“…Lossy compression reduces the original image size by removing non-vital information, i.e., this technique involves some loss of information and data that have been compressed, using lossy techniques generally cannot be recovered or reconstructed exactly [8].…”

“…Compression ratio (CR) is defined as the ratio between the uncompressed image size and compressed image size [15].…”

“…Image compression reducing the amount of data required to represent a digital image by removing redundant or non-vital data. There are different types of redundancy present in an image, such as Spatial Redundancy, Statistical Redundancy and Human Vision Redundancy [8].…”