An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolation
Pixel transitions are critical in image processing, largely depending on interpolation methods to ensure smoothness and clarity. This work focuses on two widely used image interpolation techniques: nearest neighbor interpolation and bilinear interpolation, both implemented using integrated software...
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| Format: | Article |
| Language: | English |
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REA Press
2025-03-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_209886_a7fc43355bf53ceb51c8b7fff0f2342c.pdf |
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| author | Wusat Ullah Seher Ilyas Hamza Naveed Saalam Ali |
| author_facet | Wusat Ullah Seher Ilyas Hamza Naveed Saalam Ali |
| author_sort | Wusat Ullah |
| collection | DOAJ |
| description | Pixel transitions are critical in image processing, largely depending on interpolation methods to ensure smoothness and clarity. This work focuses on two widely used image interpolation techniques: nearest neighbor interpolation and bilinear interpolation, both implemented using integrated software code. Our methodology enables each interpolation technique to be applied independently, allowing for a direct comparison of their performance. To achieve a thorough evaluation of each interpolation method, we utilize a set of essential quality assessment metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Grayscale Analysis, and Mean Squared Error (MSE). These metrics were selected to provide a balanced assessment of image sharpness, structural accuracy, and overall visual quality. The results of this study offer a detailed analysis of the strengths and limitations of each interpolation technique. These findings are intended to assist researchers and practitioners in selecting the most suitable interpolation method for their specific requirements in the image processing domain. By providing a comparative framework, this work contributes to the field by enhancing methods for assessing and optimizing image quality in digital imaging applications. |
| format | Article |
| id | doaj-art-5283e228c327442b9122f697b9827306 |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-5283e228c327442b9122f697b98273062025-01-30T12:23:51ZengREA PressBig Data and Computing Visions2783-49562821-014X2025-03-0151243610.22105/bdcv.2024.489714.1218209886An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolationWusat Ullah0Seher Ilyas1Hamza Naveed2Saalam Ali3College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.Department of Software Engineering, Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, Pakistan.Department of Mathematics, University of Management and Technology Lahore, Sialkot Campus, Pakistan.Pixel transitions are critical in image processing, largely depending on interpolation methods to ensure smoothness and clarity. This work focuses on two widely used image interpolation techniques: nearest neighbor interpolation and bilinear interpolation, both implemented using integrated software code. Our methodology enables each interpolation technique to be applied independently, allowing for a direct comparison of their performance. To achieve a thorough evaluation of each interpolation method, we utilize a set of essential quality assessment metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Grayscale Analysis, and Mean Squared Error (MSE). These metrics were selected to provide a balanced assessment of image sharpness, structural accuracy, and overall visual quality. The results of this study offer a detailed analysis of the strengths and limitations of each interpolation technique. These findings are intended to assist researchers and practitioners in selecting the most suitable interpolation method for their specific requirements in the image processing domain. By providing a comparative framework, this work contributes to the field by enhancing methods for assessing and optimizing image quality in digital imaging applications.https://www.bidacv.com/article_209886_a7fc43355bf53ceb51c8b7fff0f2342c.pdfimage processingbilinear interpolationnearest neighbor interpolationimage optimization |
| spellingShingle | Wusat Ullah Seher Ilyas Hamza Naveed Saalam Ali An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolation Big Data and Computing Visions image processing bilinear interpolation nearest neighbor interpolation image optimization |
| title | An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolation |
| title_full | An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolation |
| title_fullStr | An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolation |
| title_full_unstemmed | An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolation |
| title_short | An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolation |
| title_sort | integrated approach to image quality comparative analysis of bilinear and nearest neighbor interpolation |
| topic | image processing bilinear interpolation nearest neighbor interpolation image optimization |
| url | https://www.bidacv.com/article_209886_a7fc43355bf53ceb51c8b7fff0f2342c.pdf |
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