Abstract
Image compression plays a key role in the transmission of an image and storage capacity. Image compression aims to reduce the size of the image with no loss of significant information and no loss of quality in the image. To reduce the storage capacity of the image, the image compression is proposed in order to offer a compact illustration of the information included in the image. Image compression exists in the form of lossy or lossless. Even though image compression mechanism has a prominent role for compressing images, certain conflicts still exist in the available techniques. This paper presents an approach of Haar wavelet transform, discrete cosine transforms, and run length encoding techniques for advanced manufacturing processes with high image compression rates. These techniques work by converting an image (signal) into half of its length which is known as “detail levels”; then, the compression process is done. For simulation purposes of the proposed research, the images are segmented into 8 × 8 blocks and then inversed (decoded) operation is performed on the processed 8 × 8 block to reconstruct the original image. The same experiments were done on two other algorithms, that is, discrete cosine transform and run length encoding schemes. The proposed system is tested by comparing the results of all the three algorithms based on different images. The comparison among these techniques is drawn on the basis of peak signal to noise ratio and compression ratio. The results obtained from the experiments show that the Haar wavelet transform outperforms very well with an accuracy of 97.8% and speeds up the compression and decompression process of the image with no loss of information and quality of image. The proposed study can easily be implemented in industries for the compression of images. These compressed images are suggested for multiple purposes like image compression for metrology as measurement materials in advanced manufacturing processes, low storage and bandwidth requirements, and compressing multimedia data like audio and video formats.
Highlights
Image compression techniques are concerned with reducing the size of an image
The Haar wavelet transformation (HWT) is applied along with thresholding and some other filtering techniques on joint photographic experts group (JPEG) images to achieve high compression rate with a very minor loss of information. Applicability of this technique is tested by calculating its results and comparing it with discrete cosine transform (DCT) and run length encoding (RLE) on the basis of peak signal to noise ratio (PSNR) value and compression rate
We focused on RLE technique and Haar wavelet transform to remove this correlation between pixels to achieve a high compression rate with minor loss in finer detail
Summary
Image compression techniques are concerned with reducing the size (information or contents) of an image. This paper presents a low-cost and efficient algorithm for achieving a high image compression rate with a minor loss of redundant information. The HWT is applied along with thresholding and some other filtering techniques (averaging and subtracting) on joint photographic experts group (JPEG) images to achieve high compression rate with a very minor loss of information. Applicability of this technique is tested by calculating its results and comparing it with discrete cosine transform (DCT) and run length encoding (RLE) on the basis of peak signal to noise ratio (PSNR) value and compression rate. The comparisons of all algorithms based on efficiency and compression is discussed in section ‘‘Efficiency and compression-based comparison of each technique’’, followed by the conclusion in section ‘‘Conclusion.’’
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
