Blurry no more: Understanding image resolution

#resolution #technology #opensource #performance

Introduction
In the context of screens and monitors, pixels can be defined as tiny individual squares that light up to create an image. The resolutions(144p,240p,360p,480p,720p,1080p,4k and 8k) signifies number of pixels. Higher resolution means there will be more number of pixels which results in a clearer image. We can say it as the physics of how the light works.
Now let's get introduced to Pixel Upscaling.
Pixel Upscaling basically refers to the process where a video existing with a lower resolution(144p or 240p) is converted to a high resolution(1080p or 4k). This is done by analyzing the existing pixels and creating new pixels which will fill the gap.

Upscaling Techniques
Clever algorithms are used to estimate what new pixels can be added to fill in the gaps and create a higher resolution image.

Some common upscaling techniques:
Nearest Neighbour Interpolation - This method copies the nearest existing pixel to create new ones. It is simple, speed, computationally efficient and no new data is created. It simply relies on existing information or pixels.

Deep Learning Upscaling - It makes use of Artificial Intelligence to analyze the video and create more detailed new pixels. This method includes steps like Training the Model, Analyzing the low-resolution input, Creation of new pixels and Data Enhancement. This technique is computationally expensive.

Bilinear Interpolation - This method takes the average color of the four nearest neighbouring pixels to create new pixels. It produces smoother results than nearest neighbour but may blur the details.

Conclusion
The world of video is constantly evolving with upscaling technology being at the top. The most low-resolution videos can be enjoyed in stunning detail. So next time when you watch an old video, remember the power of upscaling. It bridges the gap between the past and the present.