CCLOP: Cross-Component Enhanced LOP Filter for Video Coding

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Yue Li, Junru Li, Chaoyi Lin, Kai Zhang, Li Zhang

Published: 2025, Last Modified: 14 Feb 2026DCC 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Recent exploration efforts in JVET (Joint Video Experts Team of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC29) has achieved progresses on neural network-based video coding (NNVC)11NNVC is also the name of the reference software for evaluating neural network-based video coding technologies in JVET. The project locates at https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/VVCSoftware_VTM. Latest version of NNVC features two normative deep tools, i.e., neural network-based intra prediction and neural network-based in-loop filtering. Specifically, the neural network-based filtering in NNVC supports three operating points, known as VLOP (very low-complexity operating point), LOP (low-complexity operating point), and HOP (high-complexity operating point). LOP filter receives more attention among these three due to its favorable performance-complexity trade-off. In this paper, we introduce CCLOP, a cross-component enhanced LOP filter. CCLOP builds upon LOP filter in NNVC but incorporates deep luma features for chroma filtering. We conduct extensive experiments to verify the effectiveness of CCLOP. Compared with NNVC-10, the latest reference software of NNVC, CCLOP achieves {-0.13%, −2.27%, −3.11%}, {-0.18%, −2.07%, −3.21%}, and {-0.03%, −1.81%, −2.51%} BD-rate changes on average for {Y, Cb, Cr} under random-access, low-delay, and all-intra configurations respectively, while maintaining the same complexity as existing LOP filter (CCLOP@16.84 kMAC/pixel, LOP@16.90 kMAC/pixel).