Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN)
In principle, a video codec is built by implementing various algorithms and their development. The next generation of codecs involves more artificial intelligence applications and their development. DCNN (Deep Convolutional Neural Network) is a multi-layer NN concept with a deep learning approach in...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2022-09-01
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| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | https://joiv.org/index.php/joiv/article/view/1012 |
| _version_ | 1811159843819487232 |
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| author | Arief Bramanto Wicaksono Putra Achmad Fanany Onnilita Gaffar Muhammad Taufiq Sumadi Lisa Setiawati |
| author_facet | Arief Bramanto Wicaksono Putra Achmad Fanany Onnilita Gaffar Muhammad Taufiq Sumadi Lisa Setiawati |
| author_sort | Arief Bramanto Wicaksono Putra |
| collection | DOAJ |
| description | In principle, a video codec is built by implementing various algorithms and their development. The next generation of codecs involves more artificial intelligence applications and their development. DCNN (Deep Convolutional Neural Network) is a multi-layer NN concept with a deep learning approach in the field of artificial intelligence development. This study has proposed a DCNN with three hidden layers for intra-frame-based video compression. DCT and fractal methods were used to compare the performance of the proposed method. The training image (obtained from the average of all down-sampled frames) is divided into several square blocks using the square block shift operation until all parts of the image are fulfilled. All pixels in each block act as input data patterns. After the training process, the trained proposed DCNN was then used to construct the feature and sub-feature image obtained through the max function operation in the feature bank and sub-feature bank. These feature and sub-feature images were then a spatial redundancy minimizer with specific manipulation techniques and simultaneously a quantizer without converting the frame's pixels to a bit-stream. The result of this process is a compressed image. Experiments on the entire dataset resulted in AAPR (Average Approximate Performance Ratio) of 147.71%, or an average of 1.5 times better than other methods. For further studies, the performance improvement of the proposed DCNN is performed by modifying its structure so that the output is direct in the form of feature and sub-feature images. Another way is to combine it with the DCT or fractal method to improve the performance of the result. |
| first_indexed | 2024-04-10T05:47:47Z |
| format | Article |
| id | doaj.art-a5660f6d20de4312913b9410cfda4b3b |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-04-10T05:47:47Z |
| publishDate | 2022-09-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-a5660f6d20de4312913b9410cfda4b3b2023-03-05T10:28:41ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042022-09-016365065910.30630/joiv.6.3.1012411Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN)Arief Bramanto Wicaksono Putra0Achmad Fanany Onnilita Gaffar1Muhammad Taufiq Sumadi2Lisa Setiawati3Politeknik Negeri Samarinda, Samarinda, 75131, IndonesiaPoliteknik Negeri Samarinda, Samarinda, 75131, IndonesiaUniversitas Muhammadiyah Kalimantan Timur, Samarinda, IndonesiaPoliteknik Negeri Samarinda, Samarinda, 75131, IndonesiaIn principle, a video codec is built by implementing various algorithms and their development. The next generation of codecs involves more artificial intelligence applications and their development. DCNN (Deep Convolutional Neural Network) is a multi-layer NN concept with a deep learning approach in the field of artificial intelligence development. This study has proposed a DCNN with three hidden layers for intra-frame-based video compression. DCT and fractal methods were used to compare the performance of the proposed method. The training image (obtained from the average of all down-sampled frames) is divided into several square blocks using the square block shift operation until all parts of the image are fulfilled. All pixels in each block act as input data patterns. After the training process, the trained proposed DCNN was then used to construct the feature and sub-feature image obtained through the max function operation in the feature bank and sub-feature bank. These feature and sub-feature images were then a spatial redundancy minimizer with specific manipulation techniques and simultaneously a quantizer without converting the frame's pixels to a bit-stream. The result of this process is a compressed image. Experiments on the entire dataset resulted in AAPR (Average Approximate Performance Ratio) of 147.71%, or an average of 1.5 times better than other methods. For further studies, the performance improvement of the proposed DCNN is performed by modifying its structure so that the output is direct in the form of feature and sub-feature images. Another way is to combine it with the DCT or fractal method to improve the performance of the result.https://joiv.org/index.php/joiv/article/view/1012video codecsintra framevideo compressiondcnn. |
| spellingShingle | Arief Bramanto Wicaksono Putra Achmad Fanany Onnilita Gaffar Muhammad Taufiq Sumadi Lisa Setiawati Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN) JOIV: International Journal on Informatics Visualization video codecs intra frame video compression dcnn. |
| title | Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN) |
| title_full | Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN) |
| title_fullStr | Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN) |
| title_full_unstemmed | Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN) |
| title_short | Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN) |
| title_sort | intra frame based video compression using deep convolutional neural network dcnn |
| topic | video codecs intra frame video compression dcnn. |
| url | https://joiv.org/index.php/joiv/article/view/1012 |
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