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Why is the quality of uploaded videos so bad?

Video compression algorithms used by platforms like YouTube and TikTok are optimized for file size reduction, not quality preservation.

This can result in noticeable loss of details and artifacts, especially in high-motion scenes.

Uploading bitrate limitation: Most video platforms cap the maximum bitrate users can upload, even for high-resolution videos.

This forces content creators to lower the video's bitrate during the upload, leading to quality degradation.

Transcoding process: When a video is uploaded, platforms will often transcode it to multiple resolutions and bitrates to support different playback devices.

This transcoding step can introduce further quality loss due to recompression.

Internet upload speeds: If a user's upload bandwidth is insufficient, the platform may aggressively compress the video to reduce the file size, resulting in poorer quality.

Platform's server capacity: When a platform's servers are overloaded, they may further compress videos to conserve resources, again leading to quality loss.

Incorrect video encoding settings: Users may inadvertently upload videos with suboptimal codec, resolution, or frame rate settings, leading to quality issues.

Video container format incompatibility: Certain video container formats (e.g., MKV) may not be fully supported by all platforms, causing them to re-encode the video during upload.

Older hardware and software: Legacy devices and video editing software may not support the latest video codecs, resulting in quality loss when uploading to modern platforms.

Lack of GPU acceleration: Without GPU-accelerated video encoding, the CPU-based encoding process can introduce quality degradation, especially for high-resolution videos.

Platform-specific quality optimizations: Some platforms, like TikTok, may intentionally reduce video quality to optimize for faster upload times and reduced data usage on mobile devices.

Color depth and chroma subsampling: Platforms may reduce the color depth and chroma subsampling of uploaded videos to save space, leading to visible color banding and loss of fine details.

Algorithmic quality enhancement: While some platforms offer AI-powered quality enhancement features, they may not always be effective, especially for videos with significant artifacts or noise.

Metadata and container preservation: When a video is re-encoded during the upload process, certain metadata and container information may be lost, affecting the overall quality and viewing experience.

Codec version incompatibility: Newer video codecs, like AV1 or VP9, may not be fully supported by all platforms, leading to the need for re-encoding and quality degradation.

Audience device compatibility: Platforms may optimize video quality based on the expected playback devices of their users, potentially sacrificing quality for smaller screens or lower-end devices.

Video stabilization artifacts: The video stabilization algorithms used by platforms can sometimes introduce visible artifacts, especially in high-motion scenes.

Lossy audio compression: While the focus is often on video quality, the audio compression used by platforms can also contribute to a subpar overall viewing experience.

Lack of dynamic bitrate adaptation: Some platforms do not adjust the video bitrate in real-time based on the viewer's network conditions, leading to quality fluctuations during playback.

Background processing delays: Newly uploaded videos may be displayed in lower resolutions initially while the platform processes higher-quality versions in the background, causing a temporary quality degradation.

Limited storage capacity: As platforms seek to optimize storage and bandwidth costs, they may prioritize compression over quality, resulting in a noticeable decline in video fidelity over time.

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