Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285158788> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W4285158788 endingPage "340" @default.
- W4285158788 startingPage "327" @default.
- W4285158788 abstract "Image compression techniques are advancing day by day. Image data’s growth rate is exponentially increasing for the increase of image capture devices, far beyond the compression ratio’s improvement. It recognizes the increasing challenges of research on improving image compression within traditional image compression techniques. The evolution of CNN methodology introduced the image compression scheme. CNN has accelerated neural network research in the present time and has reached excellent success in image computation domains. CNN has provided suitable solutions for image compression. This article proposes an image compression scheme based on histogram equalization (HE) and CNN. We have implemented two image compression schemes. The first scheme is the standard image compression technique using a convolution neural network model (IC-CNN). The second scheme is the proposed image compression model, i.e., histogram equalization (HE) and the CNN-based image compression technique (IC-HE-CNN). The IC-HE-CNN model aims to improve redundancy. Using the Python platform, these two models have been experimented with the high-resolution color images from the ImageNet dataset. We have computed the Bits per pixel (BPP), Peak Signal to Noise Ratio (PSNR), and MS-SSIM basic performance measurement parameters to evaluate image quality and compression efficiency. The experimental results show that the IC-HE-CNN model has achieved a lower BPP value, 0.2049, than the existing standard IC-CNN image compression model and the other four image compression models based on CNN. Also, obtained results indicate that the proposed model has achieved the required level of PSNR and MS-SSIM values." @default.
- W4285158788 created "2022-07-14" @default.
- W4285158788 creator A5045150773 @default.
- W4285158788 creator A5080723667 @default.
- W4285158788 date "2022-01-01" @default.
- W4285158788 modified "2023-09-29" @default.
- W4285158788 title "Image Compression Scheme Based on Histogram Equalization and Convolution Neural Network" @default.
- W4285158788 cites W2740336064 @default.
- W4285158788 cites W2791741309 @default.
- W4285158788 cites W2894234546 @default.
- W4285158788 cites W2904617026 @default.
- W4285158788 cites W2911075438 @default.
- W4285158788 cites W2912956249 @default.
- W4285158788 cites W2913664580 @default.
- W4285158788 cites W2926689860 @default.
- W4285158788 cites W2941512328 @default.
- W4285158788 cites W2981613960 @default.
- W4285158788 cites W2999925080 @default.
- W4285158788 cites W3012092446 @default.
- W4285158788 cites W3034653380 @default.
- W4285158788 cites W3035614937 @default.
- W4285158788 cites W3036781446 @default.
- W4285158788 cites W3044148544 @default.
- W4285158788 cites W3080423254 @default.
- W4285158788 cites W3131427579 @default.
- W4285158788 doi "https://doi.org/10.1007/978-981-19-1559-8_33" @default.
- W4285158788 hasPublicationYear "2022" @default.
- W4285158788 type Work @default.
- W4285158788 citedByCount "0" @default.
- W4285158788 crossrefType "book-chapter" @default.
- W4285158788 hasAuthorship W4285158788A5045150773 @default.
- W4285158788 hasAuthorship W4285158788A5080723667 @default.
- W4285158788 hasConcept C115961682 @default.
- W4285158788 hasConcept C13481523 @default.
- W4285158788 hasConcept C136943445 @default.
- W4285158788 hasConcept C153180895 @default.
- W4285158788 hasConcept C154579607 @default.
- W4285158788 hasConcept C154945302 @default.
- W4285158788 hasConcept C30387639 @default.
- W4285158788 hasConcept C31972630 @default.
- W4285158788 hasConcept C41008148 @default.
- W4285158788 hasConcept C54243161 @default.
- W4285158788 hasConcept C78548338 @default.
- W4285158788 hasConcept C81363708 @default.
- W4285158788 hasConcept C9417928 @default.
- W4285158788 hasConcept C94835093 @default.
- W4285158788 hasConceptScore W4285158788C115961682 @default.
- W4285158788 hasConceptScore W4285158788C13481523 @default.
- W4285158788 hasConceptScore W4285158788C136943445 @default.
- W4285158788 hasConceptScore W4285158788C153180895 @default.
- W4285158788 hasConceptScore W4285158788C154579607 @default.
- W4285158788 hasConceptScore W4285158788C154945302 @default.
- W4285158788 hasConceptScore W4285158788C30387639 @default.
- W4285158788 hasConceptScore W4285158788C31972630 @default.
- W4285158788 hasConceptScore W4285158788C41008148 @default.
- W4285158788 hasConceptScore W4285158788C54243161 @default.
- W4285158788 hasConceptScore W4285158788C78548338 @default.
- W4285158788 hasConceptScore W4285158788C81363708 @default.
- W4285158788 hasConceptScore W4285158788C9417928 @default.
- W4285158788 hasConceptScore W4285158788C94835093 @default.
- W4285158788 hasLocation W42851587881 @default.
- W4285158788 hasOpenAccess W4285158788 @default.
- W4285158788 hasPrimaryLocation W42851587881 @default.
- W4285158788 hasRelatedWork W2134211997 @default.
- W4285158788 hasRelatedWork W2368059753 @default.
- W4285158788 hasRelatedWork W2519629644 @default.
- W4285158788 hasRelatedWork W2787706201 @default.
- W4285158788 hasRelatedWork W2981467186 @default.
- W4285158788 hasRelatedWork W3112082857 @default.
- W4285158788 hasRelatedWork W3112580190 @default.
- W4285158788 hasRelatedWork W4285174488 @default.
- W4285158788 hasRelatedWork W4312724860 @default.
- W4285158788 hasRelatedWork W4312756947 @default.
- W4285158788 isParatext "false" @default.
- W4285158788 isRetracted "false" @default.
- W4285158788 workType "book-chapter" @default.