Upscale any video of any resolution to 4K with AI. (Get started for free)

What is the fundamental technical limitation that prevents a low-resolution video (e.g. 140px) from being easily upscanned or upconverted to a high-definition resolution (e.g. 1080 HD)?

**Pixel interpolation**: When upscaling a low-resolution video, algorithms interpolate missing pixels to fill in gaps, but this process can introduce artifacts and affect video quality.

**Nyquist-Shannon sampling theorem**: This fundamental principle in signal processing sets a limit on the maximum resolution of a signal, making it impossible to recover lost details in a low-resolution video.

**Aliasing**: When a low-resolution video is upscaled, aliasing occurs, causing jagged edges and stair-step patterns, which can be difficult to remove.

**Loss of frequency components**: When a video is compressed, high-frequency components are often discarded, making it impossible to recover lost details during upscaling.

**Blind deconvolution**: This technique attempts to reverse the effects of blurring and noise in an image, but it's an ill-posed problem, making it challenging to achieve high-quality results.

**Bicubic interpolation**: A common method for upscaling, bicubic interpolation can introduce artifacts and softening of the image, especially when applied to low-resolution videos.

**Chroma subsampling**: When a video is compressed, chroma (color) information is often subsampled, leading to a loss of color detail, which can be difficult to recover during upscaling.

**Motion interpolation**: This technique creates intermediate frames to smooth motion, but it can introduce artifacts like the "soap opera effect" and make the video look unnatural.

**Noise amplification**: When upscaled, noise in the original video can become amplified, leading to a decrease in overall video quality.

** ringing artifacts**: Upscaling can introduce ringing artifacts, which appear as ripples or echoes around edges, further degrading video quality.

**Color banding**: When a low-resolution video is upscaled, color banding can occur, resulting in visible steps or gradations in the color palette.

**Compression artifacts**: Compression algorithms like MPEG-2 and H.264 can introduce blocky artifacts, which can be difficult to remove during upscaling.

**Limitations of super-resolution**: Even with advanced techniques like deep learning-based super-resolution, there are fundamental limits to the amount of detail that can be recovered from a low-resolution video.

**Trade-offs in upscaling**: Upscaling algorithms often involve trade-offs between resolution, noise reduction, and artifacts, making it challenging to achieve optimal results.

**Physical limitations of sensors**: The quality of the original video is limited by the physical capabilities of the camera sensor, which can introduce limitations that cannot be overcome by upscaling.

Upscale any video of any resolution to 4K with AI. (Get started for free)