What is a P-frame?
A P-frame (predictive frame) is a type of frame used in video compression algorithms, such as H.264 (AVC) or H.265 (HEVC). P-frames play a crucial role in video compression by predicting the differences between the current frame and a reference frame, typically a keyframe (I-frame) or another P-frame.
Here are the key characteristics of P-frames:
Predictive Encoding: P-frames are encoded by predicting the differences between the current frame and a reference frame. They use motion compensation techniques to estimate the motion vectors and residual information. This prediction process exploits temporal redundancies in the video stream.
Smaller File Size: P-frames offer higher compression efficiency compared to keyframes (I-frames) because they only encode the changes or differences from the reference frame. By representing only the motion and residual information, P-frames result in smaller file sizes without significant loss in video quality.
Dependency on Reference Frames: During decoding, P-frames require access to the reference frames (keyframes or previous P-frames) for accurate reconstruction. If any reference frame is missing or corrupted, it can affect the decoding process and may result in degraded video quality until the next keyframe is encountered.
Lower Decoding Complexity: P-frames generally have lower decoding complexity compared to B-frames (bi-directional frames). Since they rely on a single reference frame, the decoding process is computationally less demanding.
The arrangement of frames in a video stream typically follows a pattern known as a Group of Pictures (GOP). In a GOP, P-frames are placed between keyframes (I-frames) and B-frames (bi-directional frames). This order allows for efficient compression while maintaining video quality and facilitating random access within the stream.
In summary, P-frames (predictive frames) are frames used in video compression algorithms for achieving higher compression efficiency. They encode the differences between the current frame and a reference frame, resulting in smaller file sizes. P-frames rely on reference frames during decoding, and their use contributes to the overall compression and video quality in a video stream.
Here are the key characteristics of P-frames:
Predictive Encoding: P-frames are encoded by predicting the differences between the current frame and a reference frame. They use motion compensation techniques to estimate the motion vectors and residual information. This prediction process exploits temporal redundancies in the video stream.
Smaller File Size: P-frames offer higher compression efficiency compared to keyframes (I-frames) because they only encode the changes or differences from the reference frame. By representing only the motion and residual information, P-frames result in smaller file sizes without significant loss in video quality.
Dependency on Reference Frames: During decoding, P-frames require access to the reference frames (keyframes or previous P-frames) for accurate reconstruction. If any reference frame is missing or corrupted, it can affect the decoding process and may result in degraded video quality until the next keyframe is encountered.
Lower Decoding Complexity: P-frames generally have lower decoding complexity compared to B-frames (bi-directional frames). Since they rely on a single reference frame, the decoding process is computationally less demanding.
The arrangement of frames in a video stream typically follows a pattern known as a Group of Pictures (GOP). In a GOP, P-frames are placed between keyframes (I-frames) and B-frames (bi-directional frames). This order allows for efficient compression while maintaining video quality and facilitating random access within the stream.
In summary, P-frames (predictive frames) are frames used in video compression algorithms for achieving higher compression efficiency. They encode the differences between the current frame and a reference frame, resulting in smaller file sizes. P-frames rely on reference frames during decoding, and their use contributes to the overall compression and video quality in a video stream.
Article written by
Sylvain Corvaisier
Independent streaming and iOS engineer
Last modified: January 9th, 2025
Sylvain Corvaisier
Independent streaming and iOS engineer
Last modified: January 9th, 2025
