# FDIM: A Feature-distance-based Generic Video Quality Metric for Versatile Codecs

arXiv: https://arxiv.org/abs/2604.24123 · HTML: https://mlsanity.com/paper/2604.24123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 39 of its 66 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Builds on (most cited first, 30 of 39)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels](https://mlsanity.com/paper/2312.17090/) (2023)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment](https://mlsanity.com/paper/2308.03060/) (2023)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [FVC: A New Framework towards Deep Video Compression in Feature Space](https://mlsanity.com/paper/2105.09600/) (2021)
- [Neural Video Compression with Feature Modulation](https://mlsanity.com/paper/2402.17414/) (2024)
- [Towards Practical Real-Time Neural Video Compression](https://mlsanity.com/paper/2502.20762/) (2025)
- [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019)
- [Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network](https://mlsanity.com/paper/2204.10485/) (2022)
- [A Technical Overview of AV1](https://mlsanity.com/paper/2008.06091/) (2020)
- [DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment](https://mlsanity.com/paper/2206.09853/) (2022)
- [HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content](https://mlsanity.com/paper/2304.13625/) (2023)
- [ColorVideoVDP: A visual difference predictor for image, video and display distortions](https://mlsanity.com/paper/2401.11485/) (2024)
- [AlphaVC: High-Performance and Efficient Learned Video Compression](https://mlsanity.com/paper/2207.14678/) (2022)
- [LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models](https://mlsanity.com/paper/2408.14008/) (2024)
- [CONVIQT: Contrastive Video Quality Estimator](https://mlsanity.com/paper/2206.14713/) (2022)
- [SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment](https://mlsanity.com/paper/1804.04813/) (2018)
- [StarVQA: Space-Time Attention for Video Quality Assessment](https://mlsanity.com/paper/2108.09635/) (2021)
- [RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training](https://mlsanity.com/paper/2202.08595/) (2022)
- [Perceptual Video Quality Prediction Emphasizing Chroma Distortions](https://mlsanity.com/paper/2009.11203/) (2020)
- [One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment](https://mlsanity.com/paper/2304.03412/) (2023)
- [Towards Perceptually Optimized End-to-end Adaptive Video Streaming](https://mlsanity.com/paper/1808.03898/) (2018)

## Abstract (arXiv)

Video technology is advancing toward Ultra High Definition (UHD) and High Dynamic Range (HDR), which intensifies the need for higher compression efficiency for these high-specification videos. Beyond advances in traditional codecs, neural video codecs (NVCs) have attracted significant research attention and have evolved rapidly over the past few years. The coding artifacts of NVCs often exhibit content-varying and generative characteristics, which differ from those of conventional codecs and are challenging for traditional video quality assessment (VQA) methods to capture. Therefore, VQA metrics are required to generalize across different codecs, content types, and dynamic ranges to better support video codec research and evaluation. In this paper, we propose FDIM, a feature-distance-based generic video quality metric for both traditional and neural video codecs across SDR and HDR formats. FDIM employs a hybrid architecture that integrates deep and hand-crafted features. The deep feature component learns multi-scale representations to capture distortions ranging from structural and textural fidelity degradation to high-level semantic deviations, while the hand-crafted feature component provides stable complementary cues to improve overall generalization. We trained FDIM on a large-scale subjective quality assessment dataset (DCVQA) consisting of over 16k video sequences encoded by traditional block-based hybrid video codecs and end-to-end perceptually optimized neural video codecs. Extensive experiments on ten SDR/HDR VQA datasets containing diverse, previously unseen codecs demonstrate that FDIM achieves strong generalization and high correlation with subjective assessment. The source code for FDIM and the DCVQA validation set will be released at https://github.com/MCL-ZJU/FDIM.