SCENE: Semantic-aware Codec Enhancement with Neural Embeddings

• URL: http://arxiv.org/abs/2601.22189v1
• Date: Thu, 29 Jan 2026 05:41:28 GMT
• Title: SCENE: Semantic-aware Codec Enhancement with Neural Embeddings
• Authors: Han-Yu Lin, Li-Wei Chen, Hung-Shin Lee,
• Abstract summary: We propose a semantic-aware pre-processing framework that enhances perceptual fidelity.<n>Our method integrates semantic embeddings from a vision-language model into an efficient convolutional architecture.<n>Our results show that semantic-aware pre-processing is an effective approach for enhancing compressed video streams.
• Score: 9.6489283335586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Compression artifacts from standard video codecs often degrade perceptual quality. We propose a lightweight, semantic-aware pre-processing framework that enhances perceptual fidelity by selectively addressing these distortions. Our method integrates semantic embeddings from a vision-language model into an efficient convolutional architecture, prioritizing the preservation of perceptually significant structures. The model is trained end-to-end with a differentiable codec proxy, enabling it to mitigate artifacts from various standard codecs without modifying the existing video pipeline. During inference, the codec proxy is discarded, and SCENE operates as a standalone pre-processor, enabling real-time performance. Experiments on high-resolution benchmarks show improved performance over baselines in both objective (MS-SSIM) and perceptual (VMAF) metrics, with notable gains in preserving detailed textures within salient regions. Our results show that semantic-guided, codec-aware pre-processing is an effective approach for enhancing compressed video streams.

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