Implement the frame of app(23/01/25)Implement TF Lite Interpreter by Java(23/01/25)- Implement TF Lite Interpreter by C++
- Build DNN model using the python a to z
- Add evaluation indicators i.e. PSNR, SSIM
- Expanding the subject from Image Super Resolution to other applications
- Implement the CPU, GPU co-execution for DNN task (GOAL!)
- Implement Video Super Resolution by using FFmpeg
- Refactor the VSR code more generally
- Solve the issue of storage -> it's hardcoded now
23/01/25:
- Start Toy Project.
- Finish setting up basic frame of app.
- Finish java implementation of simple super resolution app
23/01/26:
- Expand the project to video super resolution
- Create new activity for video super resolutio
- Capture frames from video by using FFmpeg
23/01/27:
- Implement split the video by fps
- Ongoing on super resolution the frames and convert them to the super resolution video
- https://github.com/arthenica/ffmpeg-kit/tree/main/android
- https://github.com/cd-athena/MoViDNN
- https://openaccess.thecvf.com/content/CVPR2021W/MAI/papers/Liu_EVSRNet_Efficient_Video_Super-Resolution_With_Neural_Architecture_Search_CVPRW_2021_paper.pdf
https://commons.wikimedia.org/wiki/Category:Video_display_resolution_480_x_270