This paper was inspired by the paper “Multi-Objective Optimization“
Video Codec is characterized by three parameters: visual quality, bitrate and complexity (or encoding times). We can’t simultaneously improve, say, the visual quality and the bitrate without sacrificing the complexity. Similarly, we can’t reduce complexity without sacrificing visual quality and/or bitrate.
This resembles Pareto optimization:
The Pareto solution in our case is a triple or a point in three-dimensional space (quality, bitrate, complexity), where no property can be improved without sacrificing another.
The set of all Pareto optimal solutions is called a Pareto frontier. In our case the Pareto frontier is a triangle, schematically pictured below:
Sometimes i add a new axis – Error Resilience, but Error Resilience is not easily quantified. Moreover such 4d Pareto frontier can’t be visualized. There is more parameter – Latency. If you need near-zero latency (e.g. for applications like Zoom) you have to sacrifice video quality and/or complexity.
Presets of an encoder (e.g. veryfast, fast, medium, slow, veryslow) are commonly determined as the Pareto optimal vectors. In the context of video encoding, the Pareto optimal vector is a set of configuration parameters for which neither the coding gain (i.e. bitrate) nor the encoder runtime can be improved without impairing the other.
23+ years’ programming and theoretical experience in the computer science fields such as video compression, media streaming and artificial intelligence (co-author of several papers and patents).
the author is looking for new job, my resume
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