Efficient Perceptual Super-Resolution (EPSR)

• Efficient Perceptual Super-Resolution (EPSR) is a framework that balances perceptual realism with computational efficiency under strict resource limits.
• It employs lightweight architectures, specialized modules, and advanced loss functions to generate high-quality images in real-time on resource-constrained devices.
• Benchmarks like AIM 2025 show that EPSR methods can rival traditional heavy models such as Real-ESRGAN while using significantly fewer parameters and FLOPs.

Efficient Perceptual Super-Resolution (EPSR) encompasses methods and benchmarks that deliver high perceptual quality in super-resolved images or video under strict computational and memory resource constraints. The EPSR paradigm is motivated by deployment in scenarios—such as edge computing, mobile devices, and ultra-high-resolution content—where large traditional perceptual SR models (e.g., Real-ESRGAN) are computationally prohibitive. State-of-the-art approaches must therefore jointly optimize for perceptual fidelity (i.e., plausible and artifact-free detail) and parameter/FLOP efficiency, as exemplified by the recent AIM 2025 benchmark, which establishes quantifiable, standardized baselines for the field (Longarela et al., 14 Oct 2025).

1. Motivations and Constraints in Efficient Perceptual Super-Resolution

The central challenge in EPSR is to replicate or surpass the perceptual quality of large models (such as Real-ESRGAN) while enforcing severe computational limits. The AIM 2025 benchmark formalizes this as a dual constraint: all contenders must use fewer than 5 million parameters and at most 2000 GFLOPs for 960×540-pixel inputs (Longarela et al., 14 Oct 2025). Motivations for such constraints include:

• Enabling real-time deployment (especially for 4K content) on edge and mobile hardware.
• Meeting energy, bandwidth, and memory budgets in portable or embedded systems.
• Addressing increasing demands in multimedia and camera platforms for “perceptual realism” on-device, rather than relegated to powerful servers.

Efficient architectures must retain the generator/discriminator composition, perceptual and adversarial loss strategies, and resilience to a wide variety of input degradations, but under dramatically reduced architectural complexity (Longarela et al., 14 Oct 2025).

2. Architectural Approaches for Efficiency

EPSR methods deploy several strategies to minimize computational footprint while preserving quality:

• Channel and Block Reduction: Techniques such as decreasing feature dimensionality (e.g., 128→96 channels in SAFMN-L (Longarela et al., 14 Oct 2025)) and reducing the number of residual/dense blocks (e.g., TinyESRGAN) yield ~80% reductions in FLOPs and parameter counts.
• Specialized Lightweight Modules: Approaches like the Edge-Enhanced Diverse Branch Block (EDBB) deliver high-frequency detail capture with reparameterization so that, at inference, the network becomes a single-path vanilla convolution (maximizing hardware efficiency) (Longarela et al., 14 Oct 2025, Wang, 2022).
• Multi-Stage or Progressive SR: Some designs implement multi-stage upscaling (progressive ×2 steps), or network branching with early exit points, enabling spatially varying processing depth or complexity.
• Discriminator Simplification and Reuse: Efficient discriminators, such as U-Net based spectral discriminators (as in the VPEG solution (Longarela et al., 14 Oct 2025)), offer adversarial guidance at reduced cost.

Table 1: Example EPSR Network Architectures Under Efficiency Constraints

Model	Main Strategy	Params (M)	GFLOPs (960×540)
SAFMN-L (VPEG)	Reduced channels, perceptual/adversarial loss	3.17	1631
TinyESRGAN	Fewer RRDBs, smaller internal size	3.52	~2000
EFDN (IPIU)	Edge-aware, reparameterized blocks	0.2–0.3	<300

Reducing block count or feature width is complemented by judicious loss selection and training regime modifications, to maintain perceptual sharpness and realism in the absence of large-scale overparameterization.

3. Perceptual Optimization and Loss Design

Maintaining perceptual quality in the efficient regime requires both advanced loss functions and training protocols:

• Perceptual Losses: VGG19 feature loss, LDL loss, or even recent innovations like AESOP loss are integrated to direct the generator toward realistic textures and structures (Longarela et al., 14 Oct 2025).
• Adversarial Losses: Lightweight discriminators still play a role in driving the generative network toward plausible high-frequency synthesis, often with adaptations (e.g., spectral or U-Net discriminators) for efficiency.
• Targeted Losses: Some EPSR solutions apply loss components that penalize deviation in high-frequency detail while relaxing requirements in smooth regions, reflecting the selective sensitivity of the human visual system.
• Training Strategies: Multi-stage training, data augmentation, and population-based fine-tuning (as well as initialization from larger reference models) are applied to avoid quality collapse under limited capacity.

4. Benchmarking, Datasets, and Metrics

EPSR evaluation employs comprehensive benchmarks designed to stress both perceptual realism and robustness:

• PSR4K Dataset: A 500-image, 4K-resolution test set, spanning 10 semantic categories and five degradation types, designed for realism and deployment relevance, with no access to pristine targets (Longarela et al., 14 Oct 2025).
• Diverse Benchmarks: Models are further assessed on PIPAL, DIV2K-LSDIR, RealSR, RealSRSet, and Real47—covering both synthetic and real-world low-to-high resolution mappings.
• Composite Scoring: Performance is measured using multiple metrics:
  • Perceptual Index (PI): Lower is better.
  • CLIP-IQA and MANIQA: Higher is better.
  • Scores are aggregated by a weighted exponential scaling against the Real-ESRGAN baseline, using λ_PI = 0.5, λ_CLIPIQA = 0.25, λ_MANIQA = 0.25, yielding a composite efficiency-perception score (Longarela et al., 14 Oct 2025).
• Runtime Measurements: Average runtime on standardized hardware (e.g., NVIDIA H100 GPU) is used to substantiate actual efficiency gains.

Table 2: Relative Performance (Illustrative Aggregate)

Model	Relative PI	Relative CLIPIQA	Relative MANIQA
Real-ESRGAN	1.0 (base)	1.0 (base)	1.0 (base)
VPEG (SAFMN-L)	~0.75	~1.23	~1.20

EPSR approaches—e.g., VPEG—were able to surpass Real-ESRGAN across all metrics while operating at only 18–19% of the full model’s computational cost (Longarela et al., 14 Oct 2025).

5. Outcomes, Observations, and Limitations

The AIM 2025 benchmark establishes that efficient perceptual super-resolution is feasible: EPSR solutions can not only replicate, but at times outperform, the perceptual quality of heavyweights like Real-ESRGAN under stringent parameter and FLOPs constraints.

Key observations:

• Lightweight models are now credible candidates for deployment in real-world, resource-constrained environments (edge devices, mobile, streaming).
• Exploiting edge/texture-enhancing modules and perceptual/adversarial loss tuning preserves high-frequency restoration, but in some cases, residual artifacts or hallucinations may escape penalization by current perceptual metrics.
• There remain open questions regarding the adequacy of PI, CLIP-IQA, and MANIQA scores, which may not always sufficiently penalize severe artifacts; this reveals a gap between metric-driven optimization and truly consistent realism.

6. Future Research Directions

Research gaps and future directions include:

• Advanced Efficient Modules: Deeper exploration of efficient building blocks—such as further exploitation of re-parameterization, distilled knowledge transfer, block/group pruning, and normalized attention mechanisms.
• Robust Metric Development: More robust and artifact-sensitive perceptual metrics, potentially leveraging self-supervised or multimodal (text, image) feedback, to avoid over-optimizing for “perceptual” scores while introducing unrealistic content.
• Modular Training: Tuning strategies such as population-based fine-tuning (multi-objective optimization), spatially-adaptive network pruning, and layer/branch gating based on content or predicted perceptual need.
• Realistic and Diverse Data: Greater focus on challenging degradations and domain adaptation to better match field conditions for images and emerging video super-resolution scenarios.
• Collaboration and Standardization: Continued refinement of standardized benchmarks (datasets, protocols, runtime baselines) to align academic and industrial efforts on practical EPSR advances.

7. Applications and Impact

Efficient perceptual super-resolution is especially relevant to:

• Edge and Mobile Deployment: Cameras, smartphones, smart displays, and AR/VR devices requiring real-time high-quality upscaling.
• Broadcast and Streaming: Adaptive bandwidth streaming platforms, which must upscale diverse content efficiently under hardware or energy constraints.
• Real-Time Imaging Systems: Medical, surveillance, or automotive systems with limited compute budgets but high demands for perceptual fidelity.
• Multimedia Editing: On-device upscaling for content creation and enhancement, where efficient models offer professional-grade perceptual improvement without resorting to cloud processing.

In summary, EPSR establishes a rigorous performance and resource baseline for the next generation of super-resolution systems where both perceptual fidelity and computational efficiency are co-optimized (Longarela et al., 14 Oct 2025). The field continues to move rapidly, with ongoing advances in architecture, metric design, and benchmarking poised to expand the impact and utility of efficient perceptual SR in practical applications.