Abstract
This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.
Highlights
Técnicas de Compressão com Baixa Perda de Imagens de Ferramentas de Corte: um estudo comparativo de medidas de qualidade de compressão
MATLAB®, and the results shown that the PCA technique presented the best values of Peak Signal-to-Noise Rate (PSNR) with low compression rates
This work accomplishes a comparative study between two distinct low-loss image compression techniques, i.e., the Lifting technique and the Principal Components Analysis, in order to select the more appropriate image compression technique for reducing the memory size to store cutting tool images, keeping the main important features
Summary
This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal. We accomplish a comparato compress large-scale, three-dimensional image tive study between the Lifting technique and data files, while keeping the most important inthe Principal Components Analysis, in order formation necessary to find patterns in the data to determine what of these two approaches is (GRGEĆ et al, 2000; LO et al, 2003; O’ROURKE more appropriate for cutting tool wear images and STEVENSON 1995; UHL, 1997). MATLAB , and the results shown that the PCA ment n images can be represented by only one sintechnique presented the best values of PSNR gle matrix U∈Rn⨯N , where each line corresponds with low compression rates. With high to a single image Ui in form of vector ai∈R N. values of compression rates the lifting technique gave the highest PSNR.
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.
