Optimized Adaptive Loop Filter in Versatile Video Coding

• URL: http://arxiv.org/abs/2607.05737v2
• Date: Wed, 08 Jul 2026 05:39:44 GMT
• Title: Optimized Adaptive Loop Filter in Versatile Video Coding
• Abstract summary: In Versatile Video Coding(VVC) standard, adaptive loop filter(ALF) plays an essential role in reducing compression artifacts.<n>We propose an optimized ALF framework, including the parallel design of GALF and CCALF, the adaptive parameter decision of GALF, and one-pass CCALF scheme.<n>Compared to VTM-8.0, the proposed method can reduce the picture buffer access from 152 to 1 and achieve roughly 25% time-savings of the ALF module.
• Score: 59.71144973605487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the Versatile Video Coding~(VVC) standard, adaptive loop filter~(ALF), including Geometry transformation-based Adaptive Loop Filter~(GALF) and Cross Component Adaptive Loop Filter~(CCALF), plays an essential role in reducing compression artifacts. However, it also has high coding complexity and requires many picture buffer accesses in the encoder that will increase external memory access and is unfriendly to the software and hardware design. Therefore, we propose an optimized ALF framework, including the parallel design of GALF and CCALF, the adaptive parameter decision of GALF, and one-pass CCALF scheme by effectively estimating the CCALF filtering distortion without conducting filter operation. Compared to VTM-8.0, the proposed method can reduce the picture buffer access from 152 to 1 and achieve roughly 25\% time-savings of the ALF module with negligible coding performance change under RA configuration. Some of the proposed methods have been adopted in the VVC reference software.

Related papers

• Deformable Wiener Filter for Future Video Coding [55.405068056883614]
  We propose a deformable Wiener Filter (DWF) for in-loop filters in Versatile Video Coding (VVC)<n>It combines the local and non-local characteristics and supervisedly trains the filter coefficients based on the Wiener Filter theory.<n>It achieves 1.16%, 1.92%, and 2.67% bit-rate savings on average compared to the VTM-11.0 for All Intra, Random Access, and Low-Delay B configurations.
  arXiv  Detail & Related papers  (2026-06-01T02:24:07Z)
• Differentiable Attenuation Filters for Feedback Delay Networks [3.8530395083350615]
  We introduce a novel method for designing attenuation filters in digital audio reverberation systems based on Feedback Delay Net- works (FDNs)<n>Our approach uses Second Order Sections (SOS) of Infinite Impulse Response (IIR) filters arranged as parametric equalizers (PEQ)<n>Our method delivers a flexible and differentiable design, achieving state-of-the-art per- formance while significantly reducing computational cost.
  arXiv  Detail & Related papers  (2025-11-25T15:01:55Z)
• In-Loop Filtering Using Learned Look-Up Tables for Video Coding [34.73130210353939]
  In-loop filtering (ILF) is a key technology in video coding standards to reduce artifacts and enhance visual quality.<n>In this paper, we propose a universal LUT-based ILF framework, termed LUT-ILF++.<n>Compared to DNN-based solutions, our proposed solution has much lower time complexity and storage cost.
  arXiv  Detail & Related papers  (2025-09-11T14:34:01Z)
• In-Loop Filtering via Trained Look-Up Tables [45.6756570330982]
  In-loop filtering (ILF) is a key technology for removing the artifacts in image/video coding standards. We propose an efficient and practical in-loop filtering scheme by adopting the Look-up Table (LUT) Experimental results show that the ultrafast, very fast, and fast mode of the proposed method achieves on average 0.13%/0.34%/0.51%, and 0.10%/0.27%/0.39% BD-rate reduction.
  arXiv  Detail & Related papers  (2024-07-15T17:25:42Z)
• Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines [5.155405463139862]
  This paper investigates the efficacy of jointly optimizing content-specific post-processing filters to adapt a human oriented video/image into a machine vision task. We propose a novel training strategy based on competitive learning principles. Experiments on the OpenImages dataset show an improvement in the BD-rate reduction from -41.3% to -44.6%.
  arXiv  Detail & Related papers  (2024-06-18T07:45:57Z)
• Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler [103.97487121678276]
  Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs. We propose a novel Knowledge-driven Differential Filter Sampler(KDFS) with Masked Filter Modeling(MFM) framework for filter pruning.
  arXiv  Detail & Related papers  (2023-07-01T02:28:41Z)
• Parallel APSM for Fast and Adaptive Digital SIC in Full-Duplex Transceivers with Nonlinearity [19.534700035048637]
  kernel-based adaptive filter is applied for the digital digital domain self-interference cancellation (SIC) in transceiver in full (FD) mode. They demonstrate that the kernel-based algorithm achieves a favorable level of digital SIC while enabling parallel computation-based implementation within a rich and nonlinear function space.
  arXiv  Detail & Related papers  (2022-07-12T11:17:22Z)
• Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions [73.33133942934018]
  2D filter parametrization technique plays an important role when designing equivariant convolutions. New equivariant convolution method based on the proposed filter parametrization method, named F-Conv. F-Conv evidently outperforms previous filter parametrization based method in image super-resolution task.
  arXiv  Detail & Related papers  (2021-07-30T10:01:52Z)
• Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion [139.71852076031962]
  We present a novel filter pruning method, dubbed dynamic-coded filter fusion (DCFF) We derive compact CNNs in a computation-economical and regularization-free manner for efficient image classification. Our DCFF derives a compact VGGNet-16 with only 72.77M FLOPs and 1.06M parameters while reaching top-1 accuracy of 93.47%.
  arXiv  Detail & Related papers  (2021-07-14T18:07:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.