Kelvin v1.0: A Neural Pre-Encoder for H.264: A standards-compliant learned preprocessor with -27.62% BD-VMAF on UVG
Marco Graziano

TL;DR
Kelvin v1.0 is a standards-compliant learned pre-encoder that improves H.264 compression efficiency by content-adaptive pixel adjustments, achieving significant BD-VMAF gains while maintaining compatibility with existing decoders.
Contribution
Introduces Kelvin v1.0, a lightweight neural pre-encoder that enhances H.264 encoding performance without altering the standard bitstream, addressing non-differentiability with a hybrid codec proxy.
Findings
Achieves -27.62% BD-VMAF on UVG benchmark
Wins on 28 of 30 clips in MCL-JCV set
Maintains compatibility with all existing H.264 decoders
Abstract
Kelvin is a lightweight learned pre-encoder that sits in front of an unmodified libx264 encoder. It applies content-adaptive pixel adjustments, bounded at +/-1/255 per channel, so that the encoder allocates bits where they matter most perceptually, while emitting a standard H.264 bitstream compatible with every existing decoder, player, and CDN. On the seven-sequence 1080p UVG benchmark, Kelvin v1.0 achieves a mean BD-VMAF of -27.62% (7 of 7 wins) and BD-VMAF-NEG of -5.18% (6 of 7 wins) relative to baseline libx264 at preset medium. On the 30-sequence MCL-JCV public set (28 unseen by training), the same checkpoint wins on 28 of 30 clips by BD-VMAF; with the two diagnosable failures removed the mean is -27.70% BD-VMAF and -5.37% BD-VMAF-NEG, consistent with UVG to within one percentage point. A central engineering challenge is the non-differentiability of H.264: we describe a hybrid…
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