Abstract
Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression ratio, the soft compression algorithm outperforms the popular classical standards PNG and JPEG2000 in lossless image compression. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images (such as medical images) can be greatly reduced with applying soft compression.
Highlights
Image compression is to reduce the required number of bits as much as possible when representing an image
Lossless compression requires the reconstructed image to be exactly the same as the original image, which leads to the compression ratio being much smaller than that of lossy compression
We adopt it as an significant criterion to measure the image compression algorithm
Summary
Image compression is to reduce the required number of bits as much as possible when representing an image. In this process, the fidelity of the reconstructed image and original image should be higher than the reference value. Lossy compression allows the reconstructed image to be different from the original image, but it is still visually similar. Lossless compression requires the reconstructed image to be exactly the same as the original image, which leads to the compression ratio being much smaller than that of lossy compression. Most of the image compression methods mainly consider three aspects to reduce the required number of bits when representing an image: coding redundancy, spatial redundancy and irrelevant information. Image compression techniques usually improve the algorithm performance from one or several aspects
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
