Video Aesthetic Quality Assessment

Updated 5 February 2026

Video Aesthetic Quality Assessment is a process that evaluates videos by integrating high-level semantic cues with low-level technical details to mirror human aesthetic judgment.
Modern approaches leverage deep learning, dual-branch fusion architectures, and CLIP-based semantic scoring to capture compositional nuances and affective responses.
Empirical benchmarks and multi-dimensional annotations validate these systems using metrics like MOS, SRCC, and PLCC, guiding improvements in interpretability and generalization.

Video aesthetic quality assessment (Video AQA) is the task of evaluating the visual and affective quality of videos as perceived by human observers, emphasizing dimensions such as composition, color harmony, creative intent, and emotional resonance, in distinction from purely technical quality measures like sharpness, noise, or compression artifacts. Recent advances in deep learning, large-scale multimodal models, and contrastive vision-language pre-training have driven a paradigm shift toward systems that explicitly incorporate semantic, compositional, and affective factors in automated video aesthetics assessment. This article provides a comprehensive synthesis of modern Video AQA research, spanning foundational models, annotation methodologies, multi-dimensional benchmarking, and technical innovations for robust, interpretable, and generalizable assessment.

1. Foundations and Key Dimensions of Video Aesthetic Quality

Rigorous assessment of video aesthetics requires explicit disentanglement of high-level semantic/aesthetic criteria from low-level technical quality factors. Human studies confirm that judgments of overall video quality on user-generated content (UGC) are universally and inevitably shaped by both perspectives: high-level semantic appeal (composition, color, meaningful content) and low-level technical fidelity (blur, noise, exposure, artifacts) (Wu et al., 2022). For example, MOS (mean opinion score) correlations confirm that both technical and aesthetic sub-scores are highly predictive of overall perceptual quality, but with distinct and sometimes independent contributions.

Key aesthetic dimensions evaluated in modern benchmarks include:

Visual Form: composition, spatial arrangement of elements, depth of field, shot scale.
Visual Style: lighting, color harmony, tone, originality.
Visual Affectiveness: emotion evoked, clarity of theme, viewer interest (Li et al., 29 Jan 2026).
Semantics: correspondence with expected content given prompts or genre.
Dynamic Qualities: fluidity of motion, temporal coherence, dynamic variety (Wang et al., 2024).
Human Feelings: subjective impressions such as excitement, calmness, or fear represented via carefully designed language prompts (Mi et al., 2023).

Leading datasets (e.g., DIVIDE-3k, MVQA-68K, VideoAesBench, AIGVQA-DB) and human annotation protocols now separately score aesthetic and technical perspectives, and in some cases collect explicit weights that raters assign to each dimension when forming overall judgments (Wu et al., 2022, Pu et al., 15 Sep 2025, Li et al., 29 Jan 2026, Wang et al., 2024).

2. Modeling Approaches: Semantics, Technical, and Fusion

2.1 Parallel Two-Branch and Fusion Architectures

State-of-the-art video AQA systems employ parallel branches:

Aesthetic Branch: Processes downsampled or sparsely sampled frames to preserve global compositional and semantic information, using backbones pre-trained on image aesthetics (e.g., AVA) (Wu et al., 2022, Shen et al., 4 Mar 2025).
Technical Branch: Processes patch grids or high-resolution clips to better identify local distortions or defects (blur, noise, compression artifacts). Pretrained on action recognition (Kinetics-400) or distortion classification (Shen et al., 4 Mar 2025, Wu et al., 2022).

Fusion mechanisms include linear weighting (empirically found: ~0.43 aesthetic : 0.57 technical (Wu et al., 2022)), dual cross-attention (Siamese architectures (Shen et al., 4 Mar 2025)), or modular late fusion (Kuang et al., 2020).

2.2 CLIP-Based Semantic and Affective Scoring

Contrastive Language-Image Pre-trained (CLIP) models underpin modern Video AQA methods by aligning visual features with high-level text prompts. The Semantic Affinity Quality Index (SAQI) measures the cosine similarity between CLIP-encoded video frames/patches and textual prompts reflecting high-quality semantics (e.g., "well-composed scene," "aesthetically pleasing landscape"), aggregated across prompts and spatial regions (Wu et al., 2023). The SAQI-Local variant improves sensitivity to local compositional errors.

CLiF-VQA extends this paradigm by incorporating prompts that explicitly encode human feelings and affective impressions alongside objective quality factors, extracting a "semantic-feelings" embedding for each video via patchwise CLIP similarity, and fusing this with a distortion-aware spatial backbone (Mi et al., 2023). Empirically, joint integration of semantic and objective (technical) prompts yields optimal alignment to human MOS in cross-dataset generalization.

2.3 Multistream and Multimodal Aesthetic Assessment

For specialized domains, such as UAV aerial videos, multimodal fusion includes additional motion and structural streams—e.g., LSTM-modeled drone trajectory features, and point cloud (SLAM-derived) scene structure—augmented alongside spatial appearance for professional/amateur classification (Kuang et al., 2020).

Emerging architectures leverage large multimodal models (LMMs) and video-language transformers for multi-dimensional and chain-of-thought interpretable scoring, offering fine-grained output per dimension (aesthetics, composition, factual consistency, etc.) with corresponding rationales (Pu et al., 15 Sep 2025, Wang et al., 2024).

3. Large-Scale Benchmarks and Annotation Protocols

Recent benchmarks such as VideoAesBench (Li et al., 29 Jan 2026), MVQA-68K (Pu et al., 15 Sep 2025), AIGVQA-DB (Wang et al., 2024), and DIVIDE-3k (Wu et al., 2022) systematically quantify model performance across aesthetic subdimensions and video sources (UGC, AI-generated, robotic, gaming, compressed). Expert human annotation pipelines emphasize the following best practices:

Multi-perspective, multi-dimensional scoring protocols, distinguishing between visual form, style, affectiveness, and explicit reasoning for each rating.
Use of both closed-ended (single/multiple-choice, true/false) and open-ended (descriptive) question formats, with validation by multiple annotators (Li et al., 29 Jan 2026).
Collection of chain-of-thought rationales for interpretability and training stability (Pu et al., 15 Sep 2025).
Pairwise ranking protocols and Z-score normalization to stabilize subjective variability (Wang et al., 2024).

A representative breakdown of VideoAesBench question and dimension coverage is tabulated below.

Aspect	Sub-dimensions
Visual Form	Composition, Elements & Structure, Shot Size, Depth of Field, Visual Subject
Visual Style	Lighting, Color Harmony, Tone, Creativity
Affectiveness	Emotion Evoked, Theme & Communication, Viewer Interest

4. Quantitative Performance and Empirical Findings

4.1 Comparative Benchmarks

On public datasets (e.g., LIVE-VQC, KoNViD-1k, CVD2014), unified models such as BVQI-Local and DOVER outperform prior zero-shot and many supervised methods even without human MOS for training, achieving SRCC values of 0.74–0.79 (Wu et al., 2023). CLiF-VQA and SiamVQA deliver state-of-the-art or highly competitive accuracy in high-resolution and UGC settings, with SRCC/PLCC up to 0.902/0.903 for fine-tuned models (Mi et al., 2023, Shen et al., 4 Mar 2025).

4.2 Ablation Analyses

Critical ablation results include:

Semantic-only branches (e.g., SAQI alone) lack sensitivity to blur/noise; technical-only branches miss content plausibility, demonstrating the necessity of joint modeling (Wu et al., 2023).
Weight-sharing between branches yields improvements in semantic sensitivity without dependence on image aesthetic pre-training (Shen et al., 4 Mar 2025).
Inclusion of both objective and subjective prompts for feelings yields superior generalization to cross-domain test sets (Mi et al., 2023).

4.3 LMM/LLM Model Evaluation

Large multimodal models show only basic capacity for video aesthetic perception, with current models achieving closed-ended accuracy <70% on VideoAesBench and with the most challenging categories being nuanced compositional cues, depth of field, and dynamic qualities (Li et al., 29 Jan 2026). Multi-prompt ensembling and the inclusion of chain-of-thought rationales (MVQA-68K) significantly improve stable, interpretable scoring and generalization (Pu et al., 15 Sep 2025).

5. Training Objectives, Losses, and Interpretability

Common training strategies include:

Correlation-based Losses: Jointly optimizing for Spearman/Pearson rank order with objectives such as monotonicity-induced and linearity-induced loss (Mi et al., 2023, Wu et al., 2023).
Multi-task Losses: Separate regression/classification for each branch and auxiliary Direct Supervision losses on perspective-specific ground truth where available (Wu et al., 2022).
Chain-of-Thought Supervision: For MLLMs, explicit token-level rationale training with curriculum schedules that gradually drop teacher-forcing promotes explicit quality reasoning (Pu et al., 15 Sep 2025).
Pairwise Ranking Losses: Hinge or cross-entropy ranking objectives for more stable, human-aligned relative assessment (Wu et al., 2023, Wang et al., 2024).

Interpretability is increasingly enforced via rationale outputs, per-dimension scoring, and open-ended QA formats, promoting transparency and debuggability in assessment systems (Pu et al., 15 Sep 2025, Li et al., 29 Jan 2026).

6. Analysis of Aesthetic Versus Technical Factors and Future Directions

Empirical studies reveal that aesthetic quality signals are encoded in preserved object semantics, spatial arrangement (rule-of-thirds, symmetry), compositional features, and scale-invariant embeddings, whereas technical quality is driven by detection of local distortions and motion artifacts (Wu et al., 2022, Li et al., 29 Jan 2026). Cross-attention mechanisms and semantic fusion enhance robustness, especially for high-resolution and complex “in-the-wild” footage (Shen et al., 4 Mar 2025, Mi et al., 2023).

Limitations persist regarding dataset representativeness, sensitivity to complex motion, and multiaspect integration. Future research directions include instruction tuning of LMMs on multi-aspect aesthetic data, finer integration of temporal dynamics, differentiated domain-specific evaluation (AIGV, UAV, AR/VR), and explicit causal reasoning for explainability (Pu et al., 15 Sep 2025, Li et al., 29 Jan 2026, Wang et al., 2024). Gathering explicit multi-perspective and intra-subject weights is recommended for future datasets to enable accurate, personalized quality-of-experience models (Wu et al., 2022).

7. Application Domains and Benchmarking Best Practices

Video AQA models are deployed in:

Content Curation and Recommendation: Automated sorting and recommendation pipelines for UGC, streaming, and social video based on aesthetic merit.
AI-Generated Content QA: Filtering, ranking, and improvement of text-to-video and generative models leveraging aesthetic scores and paired rationales (Wang et al., 2024).
Professional Filmmaking and UAV Cinematography: Objective grading, segment detection, and aesthetic path planning in aerial videography (Kuang et al., 2020).
Benchmarking and Model Development: Structured, multidimensional testbeds (VideoAesBench, DIVIDE-3k, AIGVQA-DB) yield fine-grained measurement of model strengths and weaknesses, driving continuous improvement (Li et al., 29 Jan 2026, Wang et al., 2024).

Adherence to best practices—multi-dimensional annotation, explicit separation of technical and semantic features, compositional and affective diversity in datasets, and interpretable outputs—is central to building next-generation aesthetic assessment systems.

Citations: (Wu et al., 2023, Wu et al., 2022, Shen et al., 4 Mar 2025, Mi et al., 2023, Li et al., 29 Jan 2026, Pu et al., 15 Sep 2025, Kuang et al., 2020, Wang et al., 2024)