Exploring Real-Time Super-Resolution: Benchmarking and Fine-Tuning for Streaming Content
Authors: Evgeney Bogatyrev
Khaled Abud
Ivan Molodetskikh
Nikita Alutis
Dmitriy S. Vatolin
Venue: ICLR 2026 Poster (2026)
Keywords: super-resolution
dataset
benchmark
real-time
quality assessment
video compression
Primary area: datasets and benchmarks
TL;DR: We benchmarked 11 advanced real-time SR models on a new UGC dataset for streaming content and proposed EfRLFN model to boost video upscaling quality.

Abstract: Recent advancements in real-time super-resolution have enabled higher-quality video streaming, yet existing methods struggle with the unique challenges of compressed video content. Commonly used datasets do not accurately reflect the characteristics of streaming media, limiting the relevance of current benchmarks. To address this gap, we introduce a new comprehensive dataset - $\textbf{StreamSR}$ - sourced from YouTube, covering a wide range of video genres and resolutions representative of real-world streaming scenarios. We benchmark 11 state-of-the-art real-time super-resolution models to evaluate their performance for the streaming use-case.

Furthermore, we propose $\textbf{EfRLFN}$, an efficient real-time model that integrates Efficient Channel Attention and a hyperbolic tangent activation function - a novel design choice in the context of real-time super-resolution. We extensively optimized the architecture to maximize efficiency and designed a composite loss function that improves training convergence. EfRLFN combines the strengths of existing architectures while improving both visual quality and runtime performance.

Finally, we show that fine-tuning other models on our dataset results in significant performance gains that generalize well across various standard benchmarks. We made the dataset, the code, and the benchmark available at https://github.com/EvgeneyBogatyrev/EfRLFN.

Published as a conference paper at ICLR 2026
EXPLORING REAL- SUPER-RESOLUTION: BENCHMARKING AND FINE-TUNING TUNING FOR STREAMING CONTENT
Evgeney Bogatyrev123, Khaled Abud Ivan Molodetskikh Nikita Alutis! Dmitriy Vatolin'
AI Center, Lomonosov Moscow State University
Lomonosov Moscow State University, 119991, Moscow, Russia
MSU Institute for Artificial Intelligence, Lomonosov Moscow State University
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ABSTRACT
Recent advancements in real-time super-resolution have enabled quahey-quality video streaming, yet existing methods struggle with the unique challenges of compressed video content. Commonly used datasets do not accurately reflect the characteristics of streaming media, limiting the relevance of current benchmarks. To address this gap, we introduce comprehensive dataset StreamSR sourced from YouTube, covering a wide range of video genres and resolutions representative of real-world streaming scenarios. We benchmark 1 state-of-the-ae real-time super-resolution models to evaluate their performance for the streaming use-case.
Furthermore, we propose EfRLFN, an efficient real-time model that integrates Efficient Channel Attention and hyperbolic tangent activation function novel design choice in the context of real-time super-resolution. We extensively optimized the architecture maximize efficiency and designed composite loss function that improves training convergence. EIRLFN combines the strengths of existing architectures while improving both visual quality and runtime performance.
Finally, we show that fine-tuning other models on our dataset results in significant performance gains that generalize well across various standard benchmarks. We made the dataset, the code, and the benchmark available at com/ Seemm//vgeeeeiBaaarrree//// /FRLFN
INTRODUCTION
In recent years, the demand for high-quality video streaming has surged, driven by the increasing adoption of platforms such as YouTube, Twitch, and Netflix (Grand View Research, (025)). To balance experience with bandwidth constraints, streaming services often compress video content, which may introduce artifacts such as blockiness, blurring, and loss of fine details. While real- time super-resolution (SR) methods have emerged solution enhance video quality, existing techniques struggle address the unique challenges posed by heavily compressed videos in real-world scenarios (Bogatyrev al. 2024)..
Further investigations suggest that there are limited number of SR methodologies capable of effectively upscaling compressed videos (Li et al. (021b). Additionally, there is notable scarcity of models designed for real-time video upscaling. The NTIRE challenges (Conde et al.) 2023
Ren et al., 2024) have played a pivotal role in advancing the development of real-time SR models, fostering significant progress in this domain. However, it is worth mentioning that many of these SR models have been primarily trained on datasets such as DIV2K (Agustsson Timofte 2017) or Vimeo90K (ue et al. 2019), which do not adequately represent compressed video scenarios.

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NVIDIA's Video Super-Resolune (NVIDIA VSR), learning- based SR method integrated into GPU driver, demonstrates the growing focus on real-time SR for streaming applications (Choi] 2023) However, despite its efficiency, NVIDIA VSR often fails to restore fine textures and introduces over-smoothing. limiting its effectiveness, as illustrated in Figure [ Similarly, other state-of-the SR methods, such SPAN (Wan et al. 2024) and RLFN (Kong al. (022). demonstrate promising results on standard datasets but are not optimized for the complexities of streaming content (Bogatyrev)
al. 2024).
5.0
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Model Type
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Figure 1: Left: Trade-off between user preference score and runtime speed for various 2x super- resolution models. Blue line represents Pareto-optimal front. Models achieving real-time perfor- mancePare shown in green, while slower models are red. "x" represent models fine-tuned on StreamSR dataset. Right: Examples of NVIDIA VSR artifacts compared to the proposed EfRLFN model and bicubic interpolation.
To address these challenges, we present StreamSR: comprehensive dataset of 5,200 YouTube videos (and more than 10M individual frames) covering diverse genres and resolutions, offering robust benchmark for evaluating SR methods under streaming conditions. Additionally, we propose Efficient Residual Lightweight Feature Network (EIRLFN). This model is specifically optimized for real-time applications, combining techniques adopted from prior work real-time SR and related fields. EfRLFN achieves significant improvements in terms of both objective and subjective video quality metrics while maintaining computational efficiency.
Despite the promising results of video-based models compressed video tasks, their architectures are usually too complex to meet the FPS requirements for real-time deployment. For this reason, the proposed EIRLFN model was intentionally developed as image SR model, with the goal of combining effective methodologies and optimizing existing architectures to achieve superior performance.
Our main contributions are as follows:
1. StreamSR - Streaming Super- Cepel-Resolution Dataset. We collect and analyze multiscale dataset derived from YouTube, comprising 5,200 carefully filtered user-generated videos of diverse topics and content types, and conduct comparative analysis with existing datasets.
2. EIRLEN Efficient Real-Time Super-Resolution Method. Building upon popular RLFN architecture, we thoroughly optimize it with a new block design, Efficient Channel Attention and enhanced training procedure. Our evaluations show that EfRLFN establishes a new SoTA in real-time SR, outperforming other methods in quality-complextty tradeoff, supported by extensive ablation experiments.
3. Systematic benchmarking real-time SR models. We evaluate 11 real-time super- resolution methods using our dataset, assessing their performance both with and without fine-tuning on the training set. We employ 7 different objective image quality metrics and conduct large-scale user preference study with more than 3,800 participants. User preference dataset will be also published alongside the SR benchmark.
2230 FPS Nvidia RTX 2080 GPU, which approximates potential user setup
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2
RELATED WORK
2.1
METHOSS
Recent advances in deep learning have significantly improved super-resolution (SR) techniques, with particular progress in real-time applications. Current approaches achieve varying balances between reconstruction quality and computational efficiency through innovative architectural designs.
Among real-time capable models, Bicubic++ (Bilecen & Ayazoglu 2023) combines fast reversible feature extraction with global structured pruning of convolutional layers, achieving processing speeds comparable to traditional bicubic interpolation. AsConvSR (Guo al. 2023) introduces dynamic assembled convolutions that adapt their kernels based on input characteristics, optimized particularly for mobile and edge device deployment. For high-resolution upscaling. RT4KSR (Zamfir et al.) 2023) employs pixel-unshuflling and structural re-parameterization of NAFNet blocks to efficiently handle 720p/1080p to 4K conversion. The SPAN architecture (Wan et al. 2024) features a parameter- free attention mechanism with symmetric activations to amplify important features and suppress redundant ones, reducing computational overhead by 40% compared to conventional attention modules. Similarly, RLFN (Kong al. 2022) implements streamlined three-layer convolutional structure within a residual learning framework, achieving 1080p to 4K conversion in under 15ms while maintaining strong feature aggregation. TMP (Zhang et 2024) enhances online video super-resolution by minimizing computational redundancy in motion estimation.
In addition to lightweight real-time methods, transformer-based models like SwinIR (Liang et al. 2021) and HiT-SR (Zhang et al. (2025) achieve superior quality through self-attention mechanisms and hierarchical multi-scale processing. There are other approaches SR task, such as COMISR (Li al. 2021b), which specialize in compressed video upscaling. Real-ESRGAN (Wang et al. 2021 is
a popular GAN-based method that employs high-order degradation modeling to simulate real- world degradation. Although these methods demonstrate superior results compared to real-time SR models, they are typically computationally intensive, making them unsuitable for real-time applications.
A growing line of research specifically targets compression-avee models are designed to handle distortions introduced by modern video codecs. Representative works include CAVSR (Wang et al. (202b)) and TAVSR (Wei al. 2024), which leverage codec-related features to restore severely degraded compressed sequences. Recent approaches such as FTVSR (Qiu al. 2022) and FTVSR++ (Qiu et al., 2023) utilize transformer architectures that perform self- attention across joint space-time frequency domains. Other works (An et al.] (0255) incorporate diffusion-based denoising and distortion-control modules to reduce the impact of compression artifacts during generation. These approaches typically achieve strong reconstruction quality on low-bitrate inputs but rely on computationally heavy architectures, making them unsuitable for real-time deployment.
video super-resolution, where
While all these models have demonstrated varying degrees of success in enhancing image quality through super-resolution techniques, challenges remain in effectively addressing the issues posed by compressed video content. In this paper, we build upon this foundational works aiming to evaluate and incorporate best practices to create the most suited SR model for real-time use case.
2.2 DATASETS AND REAL-TIME SR CHALLENGES
Datasets play a crucial role in training SR models. While DIV2K (Agustsson & Timofte, 2017) and Vimeo90K (uue et al. 2019) provide clean HR-LR pairs, they lack streaming compression artifacts. The YouTube-SM dataset Elbjja et al. 2016) offers diverse content but not multi-scale real-time scenarios. Additionally, several benchmark datasets are widely used for evaluating SR performance, including Set14 (Zeyde et al. 2012). BSD100 (Silval 2001), and Urban 100 (Huang al. 2015), which contain natural and structured images at fixed scales. For video super-resolution tasks, REDS (Nah et al. 2019)) provides dynamic scenes with motion blur and compression artifacts, making it suitable for training and testing temporal SR models.
Recent SR advancements focus on real-time efficiency. The NTIRE 2023 Real-Time SR Chal- lenge (Conde et al.] 2023) promoted lightweight architectures using pruning, quantization, and knowledge distillation for live applications. Its successor, NTIRE 2024 Efficient SR challenge (Ren et al. 2024), emphasized resource efficiency with adaptive transformers and multi-task learning
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Isnal Griapa:
128.34
11.25
Sunj Scom
2.24
4.52
LHRSTTA
0.247
0173
Traiiing
MX Track
Srre
EZE VEVON
Multi-stage Training
Single-stage End Training
Daard
Cavrd
L Aa JoCivau
Contractive
Edge Starpeoos
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ETRLEN frans)
Figure 2: Visual summary of the proposed EiRLEN model and the comparison with the original RLFN architecture.
combining SR with denoising and deblocking. The latest NTIRE 2025 challenge (Ren et al. 2025) further advanced efficiency by balancing runtime. FLOPs, and parameters, introducing innovations like state-space models and
distillation.
While these challenges rank real-time SR methods, their exclusion of compressed video limits their applicability to streaming content. With this idea in mind, we created a novel dataset which includes diverse videos with compression several resolutions.
PROPOSED MODEL
3.1 NETWORK ARCHITECTRRE
Although video SR models excel in standard SR tasks, their high complexity hinders real-time processing and batch training. Thus, we focus on image for our approach. The proposed Efficient Residual Local Feature Network (EfRLFN) enhances the RI.NN (Kong et al. (022) architecture through targeted modifications to improve efficiency and reconstruction quality. Figure demonstrates the overall architecture of the model and its core modifications over RLFN. EIRLFN consists of feature extraction block, main feature processing module, and an output reconstruction block. Due to their simplicity and parameter efficiency, retain the feature extractor and reconstruction modules from the original RLFN model. The key innovations of our model are focused on ERLFB block design and the training procedure.
Efficient Residual Local Feature Blocks (ERLFB) replace the original RLFB blocks in RLFNN. The primary change involves adopting tanh activation in the refinement modules instead of ReLU. This modification is motivated by recent findings (Wan et al.) (024) demonstrating that odd symmetric activation functions, such Sigmoid(x)) 0.5 tanh, preserve both the magnitude and sign of features. Unlike ReLU, which discards negative activations, tanh ensures richer gradient flow and prevents directional information loss attention maps, leading more accurate feature refinement. Empirical studies demonstrate that tanh-based activation can enhance gradient propagation in deep networks (Clevert et al., 2015) and improve performance in dense prediction tasks, including image- to-image translation (Isola et al.) 2017 Wang al. 2018).
Another important modification lies the attention mechanism used for global information ag- gregation. Efficient Channel Attention (ECA) substitutes the Enhanced Spatial Attention (ESA) mechanism used in RLFN. While ESA employs multi-layer convolutional groups for spatial attention, ECA leverages lightweight channel attention mechanism based on global average pooling and 1 x 1 convolution. This change significantly reduces computational overhead while maintaining competitive performance, validated in ablation studies.
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Video Topico
Earrie nalee and IeSiOsoe
Tarts
5200 someoan
wersopp wine
generatoo
Vadeo Retrienal
Rreendd Exceee
Figure 3: The process of video collection for StreamSR dataset. The resulting set is split into train, test, and validation parts. Zoom in for better clarity.
Table 1: Comparison of UGC Video Datasets. Quality range shows 5th-95th percentile scores of MDTVSFA metric. Duration shows the typical duration of a video clip. Image means that dataset contains only single images.
Dataset
Year
Size
Resolutions
LR?
Quality Ronge
Use-Case
Duration
YouTuhe SM
Abu-El-Haija et
2016
2016
8M videos
240p-1080p
0.45-0.55 0.58
Classification
3-5 min
Kinetics-400
240k clips
1080p
Recognition
Kinctic=7700
Carclra al 2019
2019
658k clips
1080p
0.47-0.58
Action Recognition
8-12 spc
YouTuhe-VOS5
Xur 2013
2018
4.5k videos
360p-1080p
0.46-0055
Object Segmentation
15-25 sec
YouTuhe-BB
[ear
2017
380k videos
1080p
0.50-0.60
Object Tracking
8-12
Temporal
180M clips
14fp-11880p
0.42-0.56
Language Pretraining
DIV2K
Aguusteson & Tinille 2017
2017
1,000 images
2K
0.55-0.65
Inage SR
Image
REDS (Nuh PT 2011
2019
300 clips
36lpp-220p
0.50-00.60
Vidoo SR
2-5 soe
Vimen-XIK AUI 2
2019
2019
90K clips
448p
0148-40.58
Video SR
sec
UDMIO
10K
540p-4K
0.45-0.58
Image
RealSR aa
595 pairs
4K
0.43-0.59
Real-world
Image
Videal (TCai ot 2023
2021
1,000 clips
270p-11800p
0.43-0.54
Video SR
5-10 see
StreamSK (Propsscc))
2025
5.2k videos
1600-11400
0.41-0.61
Super- Resolution
25. 30 sec
Additionaly, the reconstruction path of the ERLFB is streamlined by removing redundant skip connections and simplifying the feature smoothing step to a single 3 3 convolution. This opti- mization reduces computational fragmentation, improving inference speed without compromising reconstruction quality.
3.2 LOss FUNCTION
While RLFN's contrastive loss effectively aligns intermediate features through positive and negative sample pairs, it introduces two practical challenges: (1) sensitivity to the choice of feature extractor layers (shallow layers are required for PSNR- PSNR-orieeted tasks (Kong et al, 2022)) )) and (2) increased computational overhead due to pairwise feature comparisons. EIRLEN introduces composite loss function designed to address the limitations of RLFN's s contrastive loss while emphasizing structural fidelity and edge preservation. The loss consists of three key components:
L XCherb LChart + + AyceLvGG + SSobel
Reconstuction
Perception
Edge Sharpness
Charbonnier Loss enforces pixel-level accuracy while providing robustness outliers, addressing a limitation of the L1/L2 losses used in RLFN's early training stages. Its square-root formulation ensures stable gradients even for large residuals, making it particularly suitable for super-resolution tasks.
LChard = / IsR 2
<>
0
(2)
where JHR denotes the reference high-resolution image and IsR denotes the output of the SR module.
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VGG Perceptual Loss leverages deep features from pre-trained VGG-19 network (Simonyan & Zisserman 2014) to guide the model toward perceptually realistic outputs.
Lvag = 1 (T (T -
where (v denotes ReLU activations from the conv5 4 layer. While RLFN employs a contrastive loss to align intermediate features, our VGG loss provides analogous perceptual supervision with- out requiring complex feature pairing or negative sampling. This simplification reduces training complexity while maintaining high visual quality.
Sobel Edge Loss explicitly optimizes edge sharpness by penalizing discrepancies in gradient maps between the super-resolved and ground truth images.
Sobel = |[S(Ha)) - S(In))
(4)
where S()) applies the Sobel operator to extract horizontal and vertical edges.
bicubic
bicubic++
ESPCN
RT4KSR
SPAN
XLSR
NVIDIA VSR
RLFN
EIRLFN
Figure 4: The comparison between EIRLFN and several 4x real-time SR models.
This component addresses a critical gap in RLEN's approach, which relies on implicit edge preser- vation through contrastive learning on shallow features. By directly targeting edge quality, our loss ensures crisper and more confident reconstructions, particularly for high-frequency details. User studies confirm that such edge-enhanced outputs align with human perceptual preferences, achieving significantly higher subjective ratings (Zhang et al. 2018 Bogatyrev et al. (024).
Overall, EIRLFN outperforms RLFN with 15% faster inference via hardware-friendy) ECA and tanh activations, faster and more stable training through simplified single-stage training with carefully designed composite loss, and better quality as measured with objective and subjective metrics. We further discuss the impact of individual design choices in the Ablation Study.
4 REAL-TIME SR BENCHMARK
In this section, we detail the methodology employed for video collection process for the StreamSR dataset (summarized in Figure 3) their subsequent categorization, and overall evaluation protocol and benchmark setup.
4.1 DATASET COLLECTION
Video Retrieval We employed a Large Language Model (GPT-4o) (Achiam et al. 2023 generate search queries for collecting videos. Starting with 20 distinct YouTube categories (e.g.. travel, education, gaming), the LLM produced 100 diverse YouTube search queries per category. We then downloaded the top 100 videos for each query. The list of initial topies, LLM prompts and examples of queries are provided in the Section 17).
Filtering Only Creative Commons (CC BY 4.0) videos were included. with available resolutions of 360p. 720p, and 1440p. Videos had to maintain a consistent frame rate, verified automatically, to enable precise frame alignment. Resolutions were selected support two SR benchmark tracks: 2x

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Published as a conference paper at ICLR 2026
Table 2: Comparison of real-time SR methods with 95% confidence intervals 2x super-resolution track. Best results are in bold, second best are underlined. "T"" indicates fine-tuning on StreamSR. "Subj." represents the Brarrey-Terry subjective scores. Extended results are Section 19
Our Benchmark
Standard Benchmarks
Method
Subj.
PSNKT
LPIPS
CLIP-QAA
BSD100 SSIM PIPS.
Urban100 (SIM-/LPIPSL
DIV2K
SSIMP/1.PIPS
ArComSK
19960.11
25.055
0.214411.08
0.48611022
0.832/0.220
0,824/ 0.200
0.855
213
RIEKSR
2.4060112
26.4510.16
0,213200.09
0.59580.02
0,905 70229
0.269/0.244
0,860/0.222
10225
KI4KSR?
14
37.55=0.14
0.5340.022
0.766/00 0.223
0.644
bicuhic
2.4400 14
30. 10.32+0.21
0,076+0.005
0.44+0.05
0.752.00 0.388
0.711 00.304
0.820/0.202
1820450
ESPCN
248+0.12
30,71+0.18
0,07800.004
0.45-1022
0.774 / 0.534
0.505 00.47
0.514 /0.51
201+7
ESPON
2.490.11
35, 15
0.072 003
0.51 O2
0.814 0.236
0.840/ 0.15
0.862 180
20127
Bicubicss
2.440.13
36, 79
0.087H000034
0.52+0.01
0.228
0.832
1629445
NVIDIA VSR
2.5760114
37.40=0.13
0.08240.000
0.56gl0.01
0.758 102445
0.286/ 0.152
0.855
X
2.56H0.12
37.25+0.14
0.250H0.010
D/4H4002
0.817 0.244
0,828 151
0.864 183
429H15
SMFANg
1.3760.14
37,1440.115
0.15R:0.007
0.5120.02
0.803 0.233
0.798 0.153
0.865
327412
SAPMN
2.26H0155
15
0.122+1,006
0.53-0.02
0.813 /0.236
00.55
0,871
KLEN
2.1760155
37,0340.15
0.08%
0.5440..2
0.805 1.0238
0..47
22566
RLENT
260H0.13
17.63+0.12
0.07220.001
0.5641102
1883470.255
0,845 00135
0.881/ 178
225+8
SPAN
2.5560.12
37.4510.13
0.06610.000
0,57,0.02
0.222
0.887/
6013
SPAN
1.1360115
27.7340.12
0.06340000 003
0.6110.02
0,837 / 0.239
0.842 0.118
0.890/ 0.175
6013
3.366.14
37.85+0.11
0.059560.0005
0.65el).02
00.190
0.856/
0,892/
271=10
Non-reaf-tioe SR mudels
Real ESRGAN
3.8720.13
37.6510.12
0,04R10.007
0.6620.02
0.857 0.172
0.867/ 0.112
0 / 10.
911
BasicVSR++
4.8760.13
38.0510.13
0,03740.007
0.70
0.860 / 1.158
0,568 0.0
0,907 / 0.133
15a1
COMISR
4.3260.14
28.1550.12
0,03349.006
0.6720.03
0.894700.10 /0.161
0.871/0.108
0.913/0.125
741
(720p-+1440p) and 4x (360p-1144))) 1440p). For each resolution, the highest-bitrate stream was chosen to ensure optimal quality of the target high-res stream. We extracted the first 30 seconds of each video ( ($2,000 frames). Videas with long static intros were discarded with scene-difference filter analyzing the 1st 100th and 150th frames. This yielded collection of 5,200 videos, which we named StreamSR dataset.
Categorization To construct a diverse and representative test set, we clustered videos using SI, TI (Wang et al., 2019). bitrate, MDTVSFA (Li et al.) (021a), and SigLIP (Zhai et al.) (203)) text embeddings of the search queries. SigLIP embeddings were projected into 3-dimentional space with PCA. From each of the 20 resulting clusters, the video closest to the centroid was selected. The rest were split into training and validation sets in a 10:1 ratio.
Table compares our StreamSR dataset with existing UGC datasets. Unlike classification (YouTube- 8M, Kinetics) or segmentation datasets (YouTube-VON) VOS), StreamSR is specifically designed for super-resolution, providing aligned 160p-1440p videos with 2x/4x upscaling tracks. With longer 25-30s clips, LR-HR pairs with natural compression degradations, and broader quality range, it better represents real-world streaming scenarios than previous SR datasets (REDS, Vimeo-90K).
4.2 BENCHMARK SETUP
For benchmarking and fine-tuning, we selected a comprehensive list of SR models based on the year-to-year results of the NTIRE challenges. ranging from classical approaches like bicubic, bieu- bic++ (Bilecen & Ayazoglu 2023) and ESPCN (Talab et al. 2019) up to the recent SoTA methods
like SMFANet (Zheng et al. 2024), RLFN (Kong et al., 2022) and SPAN (Wan et al. 2024). We
had also included a proprietary NVIDIA VSR model to the comparison. Table 5 in the Appendix summarizes the information about the tested models.
We selected a diverse set of objective metrics that assess different aspects of SR algorithms' perfor- mance. PSNR and SSIM (Wang al., (004) serve as fundamental measures of pixel-Ievel fidelity and structural similarity. LPIPS (Zhang et al. 2018). on the other hand, goes beyond traditional metrics, and employs deep features to better align with human perception. Aside from Full-Reference metrics, we also included No-Reference (NR) models in the comparison. They assess the quality of sepor-re images independently, without comparing them to the real high-resolution examples. Specifically, employed 2 NR metrics: MUSIQ (Ke al. 2021), ), powerful transformer based model that excels in real-world content assessment, and CLIP-IQA (Wang et al.) (203a). a popular model that leverages vision-language correspondence capture semantic preservation in SR outputs.
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1ot block
3rd block
Eth block
RoLU
EIRLEN
Proference Pastee E90 PM
adidiig amoile
Sigmold-O..
Input
25.4%
55.0%
57.3%
19-1
77.45
TE 77
63.2%
11.5%
1175
Tanh
15-9%
(A)
(b)
Figure 5: (a) Pairwise preference evaluation EIRLFN against other real-time super-resolution methods. (b) A comparison of the output feature maps from the first, third, and sixth ERLFB blocks. The features are taken from the output of the ECA block within each ERLFB.
We evaluate the alignment of these metries with human preferences in Section 15 proving their applicability to the super-resolution quality assessment task.
The subjective comparison still remains the best option for evaluating SR performance. Since user studies can often be expensive, we preliminary excluded models that demonstrated subpar performance based on objective metries. To evaluate and rank the performance various models from subjective perspective, we conducted a pair-wise crowd-sourced comparison utilizing the Subjectify.us us (Subjectify. us 2025) service. Comparison included 11 SR models (+ Ground Truth)
and 660 video pairs in total.
total of 3,822 unique individuals participated in our subjective evaluation. Consequently, the final subjective scores were derived from the 37.184 valid responses, applying the Bradley-Terry model (Bradley& Terry 1952). More detailed information on this study provided in Section 14
EXPERIMENTS
Fine-tuning To ensure fair benchmarking of each model, we pre-trained all tested models on the training set of our dataset and evaluated the results of both pre-trained and non-pre-trained checkpoints. a model did not have pre-trained weights for a specific upscale factor (2x or 4x), we trained it from scratch and utilized only the trained version. Additional information on the training can be found in Section
Validation the StreamSR Table 2 (left) shows that the proposed model, EIRLFN, achieves superior performance on the test set of our dataset among the real-time models. The performance increase generalizes well across various objective metries, and is further confirmed by the user study (()). The results also indicate that SPAN, RLFN, and ESPCN models benefit significantly from fine-tuning on our dataset: there is notable positive performance gap in both objective and subjective evaluations. After tuning, some of these models outperform the proprictary NVIDIA VSR model in user preferences (T. We also include some non-real-time SR models for comparison.
Figure 5(a) demonstrates the results of pair-wise subjective comparison with other SR models: evidently, users largely prefer the outputs of EIRLFN over other methods. Specifically. EIRLFN is favored over NVIDIA VSR in 77.4% of cases. The only competitive alternatives (SPAN-tuned and RIFN--uned) are also trained on the StreamSR training set.
Validation Other Datasets We validated EIRLEN and other real-time SR methods on five
widely recognized datasets: Set14 (Zeyde et al. 2012), BSD100 (Silva) 2001). Urban100 (H uang al. 2015), REDS (Nah et al. 2019), and the validation set of DIV2K (Agustsson & Timofte 2017). The average metric results are presented in Table 2 (right). EIRLEN consistently outperforms all competing approaches across all evaluated scenarios, achieving superior performance in both 2x
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and 4x resolution upscaling. Other quality assessment metries, well the 4x dataset tracks reported in Section
Figures 4 and 6 b) provide visual comparison between different models, further demonstrating the effectiveness of EIRLFN real-time super-resolution Visual comparisons clearly demonstrate EfRLFN's advantages: it produces sharper images with more accurate details, while competing models often struggle to reproduce clear boundaries and fine textures, resulting in noticeable artifacts blurring in challenging regions.
Our experiments reveal that training on our proposed dataset not only benefits EfRLEN but also improves the performance of other competing models, evidenced by the quantitative results in (particularly noticeable in cases such as SPAN and RLFN).
Table 2
0.88
XLSR
SPAN
RLFN
Best SSIMSSIMMO 868
Before Fine Fine-tunniin -tenttnning
0.96
Best SSIM Score
0.94
0.82
After Fine- Fine-tunninn teee-tunning
100
200
300
400
500
Training Epochs
Charbonnier
VGG Sebel
Sobel
Charbonnter vGG
VGG
LMPS
Costrastive Losa (RLFN)
Figure 6: (a) The impart of loss functions the training of EfRLFN 4x track of the proposed dataset. "Contrastive Loss" represents the original training procedure of RLFN model. (b) The comparison between SR models before and after fine-tuning on our StreamSR dataset.
5.1 ABLATION STUDY
Loss Analysis Figure illustrates the impact of different loss functions on the SSIM performance of EfRLFN during training on the 4x track of our dataset. The full combination of Charbonnier, VGG, and Sobel losses (blue line) achieves the highest SSIM score of 0.865, clearly outperforming all ablations. This demonstrates the complementary strengths of each loss: Charbonnier contributes to structural accuracy, VGG to perceptual consistency, and Sobel to edge preservation. Ablating any component results noticeable drops, particularly the removal Charbonnier or VGG, which significantly impairs SSIM convergence. Simpler loss terms like L1. L2, and LPIPS perform substantially worse throughout training, highlighting the importance of combining both distortion- based and perceptual components for optimal SR quality. Notably, approach outperforms RLFN in both quality and training efficiency. Unlike RLFN, which employs a two-stage training procedure with L1 and contrastive losses, train our network end-to-end, achieving superior visual quality while reducing training time by 16%.
A Architectural Analysis Table presents ablation study isolating the effects of activation functions and attention mechanisms in EIRLFN. The combination of hyperbolic tangent activa- tion and ECA attention yields the best over- all performance, achieving the highest SSIM, strong perceptual quality, and the fastest run- time with minimal parameter count. Replacing tanh with a shifted sigmoid noticeably degrades SSIM and LPIPS across both attention variants. Similarly. using the heavier ESA attention block leads to lower frame rates and marginal gains in
Table 3: Ablation Study on Activation functions and Attention modules for EIRLFN. The best re- sults appear in bold. (zx) denotes a sigmoid func- tion.
Components
Metries
Activation
Attention
SSIM
LPIPS
FPS
Params
tank fank
BCA
0.865
0.173
314
0.37M
ESA
0.863
0.171
234
0.4M
S(x)
0.5
ECA
0.856
0.179
305
0.37M
8(m)
0.5
ESA
0.852
0.181
237
0.4M
ReLU
BCA
0.847
0.184
303
0.37M
ReLU
ESA
0.849
0.182
235
0.4M

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LPIPS at best. These results indicate that tanh activation paired with the lightweight ECA mod- ule provides optimal balance between quality and efficiency for real-time super-resolution.
Figure ()b) shows output features from each ERLFB block of model trained with different activation functions. Note that non-odd activation functions (ReLU) result poor feature quality. Compared Sigmoid 0.5, tanh activation preserves more high-frequency features, which correlates with better detail restoration. This results in more detailed and cleaner outputs from the EfRLEN model.
5.2 ONNX RUNTIME RESULTS
To complement the PyTorch benchmarks, we exported EIRLFN, RLFN, and SPAN ONNX and evaluated them using ONNX Runtime (v1.19.2) with both CUDA and TensorRT (v10.4.0) execution providers. All experiments were conducted on an NVIDIA RTX A6000 GPU using CUDA 11.7. The models were exported and executed in FP16 precision, and latency was measured on 360x480 inputs for both 2x and 4x upscaling.
As shown in Table 4 EIRLEN consistently achieves lower latency than RLFN across both execution providers and surpasses real-time throughput (2 30 FPS). The model also benefits significantly from TensorRT optimizations, further demonstrating its suitability for deployment-orrieneed inference engines.
Model
CUDA (ms)
TensorRT (ms)
Speedup
FPS (TRT)
Subjective Score
2x results
RLFN
30.76
29.12
1.06x
34.3
2.69 0.15
SPAN
16.29
10.46
1.55x
95.6
2.55 + 0.12
EIRLFN (ours)
17.43
12.07
1.44x
82.8
3.33 + 0.14
results
RLFN SPAN
38.24
32.48
1.18x
30.8
4.32 + 0.13
17.20
10.86
1.58x
92.1
3.14 + 0.12
EIRLFN (ours)
20.25
14.66
1.38x
68.2
4.52 +: 0.13
Table 4: ONNX Runtime results for 2x and upscaling. FPS is measured with TensorRT.
CONCLUSION
Our research advances real-time super-resolution by introducing comprehensive multi-scale dataset of 5,200 compressed videos, specifically designed for streaming applications. This dataset, coupled with systematic benchmarking of 11 SR models, provides valuable performance insights and establishes robust evaluation framework. All resources including the bench- mark, dataset. and raw subjective comparison data (37,184 responses) are publicly available com/
These subjective evaluations can serve as valuable data for training video quality assessment methods.
We further propose EiRLFN, an efficient SR model with novel architectural innovations and a compos- ite loss training strategy, supporting both video and image inputs. Extensive evaluation demonstrates EIRLEN's superior performance, delivering quality improvements and practical efficiency gains over existing methods. By combining carefully designed datasets, comprehensive benchmarking insights, and optimized model architecture, our work provides a complete foundation for advancing real-time time SR research. Since EIRLFN is designed for single-image SR, exploring lightweight temporal tensions represents an important direction for future work. The released resources, including raw subjective data, enable future work in both super-resolution and perceptual quality assessment.
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7 ACKNOWLEDGEMENTS
This work was supported by the The Ministry of Economic Development of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000C313925P4400002 grant No 139-15-2025-012).
The research carried out using the MSU-270 supercomputer of Lomonosov Moscow State University.
8 REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we have made the following resources available. The complete source code and pre-trained weights for the proposed EfRLFN model are included in the supplementary materials. A detailed description of the video collection and preprocessing pipeline from the dataset is provided in Section 4 Furthermore, the full set of experimental results, including additional ablations, can be found in Section 19
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APPENDIX CONTENTS
This appendix provides additional details, experiments, and analyses to support the main paper. Below is a summary of each section:
Section
10
Discussion of use-cases, limitations, and potential future improvements.
Summary of real-time super-resolution (SR) models evaluated, including architectures and training configurations.
Section
Section 12
Comprehensive description of the experimental setup.
Section
13
Analysis of video pairs sourced from YouTube in comparison to bicubic
downscaled LR.
Section 14
Full methodology and results of the crowd-sourced perceptual evaluation.
Section
15
Ablation study on visual quality metrics and their impact on evaluations.
Section
16
Ablation study on activation function replacements in the SPAN model.
Section
17
Prompts and sampled queries used for dataset collection.
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Section 18
Extended visual comparisons of SR model outputs.
Section 19
Expanded benchmark results (4x upscaling, additional metrics/datasets))
Section
Dataset visualization and download links for the proposed StreamSR dataset.
10 DISCUSSION AND FUTURE WORK
Computational Trade-offs. Our benchmark intentionally excludes non-real-time super-resolution methods due their low FPS. As visualized in Figure state-of-the-art methods like RealESR- GAN (Wang et al., 2021) and SwinIR (Liang et al., 2021) achieve marginally better perceptual quality but fall far below the 30 FPS threshold required for real-time upplications on consumer GPUs (NVIDIA RTX 2080 GPU, 720p to 1440p upscaling). For instance, Omni-SR. the fastest non-real-time method in our comparison, operates 16 FPS significantly slower than SPAN, which at 60 FPS was the slowest real-time method we tested.
Codec Coverage Constraints. The study's focus on YouTTube-soureed video content naturally limits our evaluation to VP9, H.264, and AVI codecs. While these account for most of web video traffic, specialized codecs used in professional settings (e.g., VVC) may exhibit different compression artifacts that could impact super-resolution performance.
Societal Impact and Potential Misuses. Like all enhancement technologies, our model carries dual-use risks, such as unauthorized upscaling of low-resolution surveillance footage beyond legal limits or generating synthetic high-resolen regoutesolution content from deliberately degraded sources.
Future Work. The real-time super-resolution technology offers significant benefits, including high- resolution streaming with lower bandwidth, which is particularly valuable for developing regions, but it also poses risks, where degraded content is enhanced appear original. To support reproducibility and future research, we provide the complete implementation code, pretrained weights, validation datasets, and raw subjective evaluation scores supplementary materials this comprehensive package will enable other researchers validate our findings, develop improved quality assessment methods, and advance real-time super-resolution techniques. Furthermore, the proposed StreamSR dataset and preference scores can used to enhance existing super-resolution quality assessment techniques.
11 REAL-TIME SR MODELS SUMMARY
This section summarizes the real-time resor-resolution models evaluated in our study. Table provides comparative analysis of their key specifications, including model size (parameters), computational complexity (FL.OPs,, supported upscaling factors, architectural design, and training datasets.
Table 5: Model Specifications of Real-Time SR Methods. Architectural details and scaling capabilities
shown.
Method
Params (M)
FLOIS (G)
Upocale Factors
Architecture Type
Training Data
ESPCN
Talaab et
2019
0.04
2.43
2x.3x,4X
Sub-pixel CNN CNN CNN-Attention
DIV2K
XLSR
Ayazogii
2021
0.03
1.81
4x
DIV2K
RLFN
Kong 1
0.40
DIV2K
AsCom'SK
(Uio el 3
2.35
2x
Assembled Convolution
DIVIK-FFickr2K
Nyazoglu
NVIDIA
VSK Choi 211
3x,3x,4x
CNN-Attentio
Proprictary
RT4KSR
Zamhi ct
0.05
2x
CNN-Attcttin
DIV2K-FFickr2K
SAFMN
Sun
0.23
4%
CNN with Feature modulation
DIVIK-HiickZK
SMFANet
Zhena al 2024
0.20
4x
CNN with Feature modulation
DIV2K-Flick2
free
DIVIK-Flickr2
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Table 6: Performance Comparison of Training Strategies DIV2K.
Model
LR Type
PSNR
SSIM f
LPIPS L
ERQA
SPAN
Synthetic
32.645
0.834
0.227
0.489
Real LR
33.511
0.847
0.206
0.501
Synthetic
32.718
0.845
RLEN
Real LR
33.816
0.855
0.182
0.535
Synhetic
33.983
0.859
0.179
0.529
EIRLEN
12 EXPERIMENTAL SETUP
We trained each model for 500 epochs, following the training processes described in the original papers. All models were evaluated on 720p videos with 2 upscaling to 1440p resolution. For FPS measurements, we ran each model on an NVIDIA GeForce RTX 2080 GPU and calculated runtime on the same test sequence of 100 frames, averaged across sequential runs. During EfRLFN training we set loss coefficients ACharb 1, AvaG 10 and Asobel = 10 for normalization purposes.
13 THE IMPACT OF USING LR-HR PAIRS
SPAN, RLFN, and EIRLFN models trained using HR-LR pairs from our dataset show better perfor- mance (Table ( compared the same models trained using bicubic downsampling process. the latter case, the HR images from our dataset were downsampled to generate L.R images using bicubic interpolation. This observation highlights the effectiveness of collecting domain-specific HR-LR pairs directly from YouTube, demonstrating that these pairs are more representative and beneficial than synthetic bicubic downsampling.
14 CROWD-SOURCED STUDY DETAILS
The evaluation was organized into two distinct tracks: one focusing on 2x SR and the other on 4x SR. Given the limited number of assessors with screen resolutions exceeding Full HD, opted to present each test video as a center Full HD crop and showed videos sequentially.
During the experiment, participants were presented with pairs of videos generated by two randomly selected SR models. They were tasked with selecting the video that exhibited fewer visual artifacts: option indicate that the videos were "indistinguishable" was assessed by total of 30 participants, with each participant evaluating 10 pairs of videos.
also provided. Each pair of videos
To ensure the integrity of the results, we included verification questions with defined correct answers among the 10 pairs. Responses from any participant who failed to answer one or more of these verification questions correctly individuals participated in our subjective evaluation. The final subjective scores were derived from the 37,184 responses, applying the Bradley-Tery model (Bradley & Terry 1952).
excluded from further analysis. A total of 3,822 unique
Figure g summarizes our setup for the study and visualizes the interface of Subjectify, service used for frowd-sourcing
Before completing the comparison, each participant was shown the following instructions:
You will see video pairs shown one after another. For each pair, select the video with better visual quality (sharper, with fewer distortions like blurring or pixelation). If they look identical, choose "Indistinguishable""
Be careful- there are verification questions in this test. Incorrect answers on verification checks will lead to rejection.
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15 CORRELATION OF METRICS WITH SUBJECTIVE SCORES
Figure 7 presents the correlation analysis between objective video quality metrics and human subjec- tive evaluations. Our experimental results demonstrate that four metrics ClipIQA, ERQA, L.IPS, and MUSIQ show significant correlations with subjective quality assessments.
These findings align with prior research in three key aspects: (1) the superior performance of feature- based metrics (LPIPS, MUSIQ) over traditional approaches, (2) ERQA's specialization for restoration quality assessment, and (3) the poor correlation of PSNR with human perception, consistent with other works (Huynh- Thu & Ghanbari 2008 Bogatyrev et al. 2024).
1.00
Subjective
1.00
0.53
0.47
0.34
0.11
0.75
CUP-IAA
0.79
1.00
0.62
0.61
0.59
0.49
0.44
0.24
ERQA
0.76
8.62
1.00
0:30
0.38
0.19
0.30
4.113
LMPS
0.73
8.61
0.30
1.00
0.56
0.36
0.36
0.58
MUSKQ
0.53
0.39
0.45
0.41
0.22
-2.30
MOTVSFA
1.00
-0.75
1.00
Swajenba
BRGA
URRS
MUsi
MOTrEE
PSR
1.00
Figure 7: Pearson Correlation between objective quality metrics and subjective scores.
Alpvss arospens Cheomium um based unquueeee onlyn test penson
Valcationn
Tasks
Sccetin paint Ful-dciees mode only
Werfhediin gxetiim
feadee in comrpann
obidia vidale
Pepment
Wive bettet
quoalkon were disercee
Ohe 21 platbem
Tedistigg braza"
purtlat
dowetcoeiing
GLEST
GUEST
Dipest
the best
Fr:: the placbck
Secord dis Dlebback
Figure 8: An overall scheme of the crowdsource subjective study described in Section 14 A screenshot of the interface of crowdsource subjective platform.
16 IMPROVING SPAN WITH HYPERBOLIC TANGENT ACTIVATION
Our experiments demonstrate that replacing the shifted sigmoid (Sigmoid((z) 0.5) with hyperbolic tangent (tanh) activation in SPAN leads to consistent improvements over the original formulation. Table compares both variants across standard benchmarks:
17

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Published as a conference paper at ICLR 2026
GT
bicubic
bicubict*
ESPON
SPAN
XLSR
NVIDIA VSR
RLFN
SPAN tanh
ERLEN
Gl
bicubie
F FS9SN
KUSA
SPAN
Dicubic-
KVTOTA VER
RLEN
SPAN tanh
ERLFN
Figure 9: The comparison between several 2x real-time SR models.
Table 7: Performance Comparison: SPAN (a = 0.5) vs. SPAN (tanh).
BSD100
Urban100
DIV2K SSIMM
Model
SSIMM
SSIMALPISS
SSIMPLLPPSS
SPAN (ar 0.5)
0.836 / 04122
0 / 0.239
0,477 0.1118 0,853 0.1115
0.890 0.175
SPAN (tanh)
0.837 0.140
0.541 / 0.228
0.891 0.167
17 LLM-GENERATED QUERIES
Table presents the description of video categories used our study, including their descriptions and the corresponding prompt templates used for query generation. Table 8 complements this by providing representative examples of L-ge-rree search queries organized by these categories, demonstrating the diversity of content collected for our benchmark dataset.
18 VISUAL COMPARISON
Figure 9 compares real-time SR models on DIV2K. Figure 11 demonstrates significant quality gains after StreamSR fine-tuning, particularly edge preservation and temporal stability.
19
FULL RESULTS
Table presents comprehensive full results of our benchmark. Tables 10 and 11 provide full validation results other datasets, including Set14 (Zeyde et al. 2012) and REDS Nah et al. 2019).
18

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Published as a conference paper at ICLR 2026
XLSR
SPAN
RLFN
Before Fine- Fine-tunning
P95
Model Type
0.10
Nin Rloa--Time Boal Tinco
After Fine Fine-tunme -ee-tunning
Sicali**
0.08
171PS
0.06
0.81M
0.3M
Okan Sit
Switlht
20M
PVRT
10
50
100
1000
Ung
Figure 10: Trade-off between LPIPS and run- time speed for various 2x super-resolution mod- els.
Figure 11: Visual quality comparison before and after fine-tuning on our dataset.
6.00
Vimen90K
CLIC2022
Comparison Dataszt
6.0
Ours
MITVVSPA
0:50
VimelisKK
CLIC2003
Codecs Comparison Dutaset
0.98
Figure 12: Distribution of Google Spatial and Temporal Wang et al. 2019) information and MDTVSFA (L et al. 202 for videos from different datasets.
19

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Published as a conference paper at ICLR 2026
Table 8: LLM-generated queries examples.
Sports
Animals
Human Activities
1. "highlights of football"
1. "wildlife documentary"
1. "people dancing"
2. "sports slow motion"
2. "cute animal videos"
2. "cooking tutorials"
"extreme sports action"
3. "slow motion animals"
"fitness workouts"
4. "basketball dunks"
4. "pets playing"
4. "street performers"
"soccer goals"
5. "nature interactions"
"DIY projects"
Art and Crafts
Maero Videos
Historical Footage
"time-lapse painting"
1. "macro insects"
1. "historical footage"
2. "sculpting techniques"
2. "close-up flowers"
2. "old documentaries"
"DIY crafts"
"macro techniques"
"historical events"
4. "artistic creations"' "watercolor tutorial"
4. "tiny world"
4. "classic film clips"
5. "everyday objects"
"moon landing"
Animated Content
Science
Nature/Landscapes
"animation films"
1. "science experiments"
1. "nature landscapes"
2. "animated scenes"
2. "physics demos"
2. "time-lapse nature"
"cartoon clips"
"chemical reactions"
"ocean waves"
4. "stop motion"
4. "biology explained"
4. "forest scenery" "mountain views"
"3D animation"
"science phenomena"
20

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Published as a conference paper at ICLR 2026
Table 9: YouTube Content Categories and Query Generation Prompts.
Category Name
Category Definition
Query Generation Prompt
"Generatee 100 diverse YouTube search queries about nature landscapes, including scenic views, wildlife habitars, waterfalls, forests, and time-lapse
Nature and Landscapes
Scenic natural environments and goographical features.
nature videos."
"Provide 100 Tube search queries related to urban environments, such as cityscapes, street photography, architectural marvels, urban exploration, and drone foolage of cities."
Urban Environments
Human-made structures and city landscapes.
"List 100 YouTube search queries for sport content, including game highlights, athlete interviews, training routines, extreme sports, and
Sports
Athletic activities and competittve physical games.
sports analysis."
"Suggest Youlube search queries about animals, covering wildlife documentaries, pel care lips, rure animal sightings. funny animal compilation,, and animal behavior studies."
Animals
Living organisms from the animal kingdom.
"Generate 100 YouTube scarch queries focused on human activities, such as daily routines, cultural festivals, street performances, social experiments, and work life documentaries."
Human Activities
Actions and behaviors performed by people.
"Create 100 YouTube search queries for and crafts, including painting tutorials, DIY projects, sculpture making, digital art timelapses. and cralt
Art and Craits
Creative works and handmade objects.
ideas for beginners."
"Provide 100 YouTube search queries for macro videos, such close-up insect footage, microscopic water droplets, detailed textures, and extreme
Macro Vidcos
Extreme closc-up footage of small subjects.
close-up photography."
"List 1OD You YouTube search queries for historical foolage, including old war vidoos, vintage city life, archival documentaries, and restored historical
Historical Footage
Recorded material from past events.
clips."
"Suggest 100 YouTube search queries for animated content, like short animated films, 3D animation breakdowns, cartoon compilations, and motion
Animated Content
Moving images created through unimattion techniques.
graphics tutorials."
"Generate 100 YouTube search queries about scientific demonstrations,, including chemistry experiments, physics simulations, biology dissections, and engincering prototypes."
Science
Illustratioss of scientific principles.
"Provide 100 You Tube search queries for technology reviews, such as smartphone comparisons, gadget unboxings, software tutorials, and
Technology Reviews
Evaluations technological products.
futuristic tech showcases."
"List 100 You Tube search queries for cooking and recipes, including easy meal prop, gourmet disbes, baking tutorials, street food tours, and kitchen
Cooking and Recipes
Food preparation instructions and techniques.
hacks."
"Suggest YouTube search queries travel vlogs, covering backpacking adventures, luxury travel guides, hidden tourist spots, and cultural
Trivel Vlogs
Video blogs documenting travel experiences.
immersion experiences."
"Generate 100 YouTuhe search queries for fitness and workours, like home exercise routines, gym training tips, yoga sessions, and body
Fitness and Workouts
Physical exereise routines and training.
transformation stories."
"Provide 100 YouTube search queries for music performances. including live concerts, acoustic covers, music festival highlights, and street
Music Performances
Live or recorded musical presentations.
musician videus." 7
"List 100 YouTube search queries educational tutorials, such math problem solving, language learning. coding lessons, and science
Educational Tutorials
Instractional content for learning.
explainers."
"Suggest 100 Tube search queries for gaming content, like walkthroughs, esports tournaments, game reviews, speedruns, and funny gaming
Gaming Content
Video game-related material.
moments."
"Generate 100 You Yobe search queries for DIY projects, including home improvement hacks, hundmade cralls, upcycling ideas, and woodworking
DIY Projects
Do-it- yoursel creative endeavors.
tutorials."
"Provide 100 YouTube search queries for drone footage, such as acrial nature shots, city flyovers, mountain landscapes, and cinematic drone
Drone Footage
Actial vidco captured by drones.
cinematography.
"List 100 You Tube search queries for time lapse videos, including sunset transitions, city day-to-night changes. plant growth, construction progressions"
Time-lapse Videos
Accelerated video sequences showing change over time.
21

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Published as a conference paper at ICLR 2026
Table 10: Comparison of real time SR models on popular benehmarks (2x upscaling). Best results in bold, second best underlined. 47" indicates fine- fine-tuning tee-tunnng our dataset.
ISNRY/SIMMTL /LLPPPS
50.2/0 10.2//.66000.222 221
30. 10.50...0 8640 186
30. W...O.......22000 206
292
29. N02.832202222 270
30 50-4/0 86200 180
J/O 8580 180
30. S. M6.00 183
30 40040 86500 177
30.70 30.7/0.8 871/0.115 175
30.6/L876600 176.760611111 175
31 31.10. 88 (O) 178
31.200 31.2/0.88 8870 168
N0..0000000011111 175
31.80089200114 145
26. MA. 1/0.514005555.
DIV2K
PSNRISSSIMTI /1LLPPPPS
26. 00.770.758/0.200 268
26 SV. 000.765502224 245
27. 3...0...880.... 1810 233
25 SW.. 70..9900//00.333 11/.118//333 31
23 1...40007770-001 478
26 200.77200222 215
26 41..400.55//... 150/ 208
26. NO.. WwW.7700..2.... 26
26 700.6600..000 209
27 777.00.777702000 202
26. W00.7700.0 2033
27. 7V5 SO LO 2006
27 777660.9988..118 186
27 7O0 20S
28 MO081141177
25 7000000003344
REDS
PSNR PSNR//SSIME /SSTI /LPIPS
28. 90. 9..00888/022311
29. 100 70.78990.210
20. 10..0..001 N01/0.205
200.72000300
28. 1,0,74
25 5180.432
19.000775500 7950000000011110 185
28 710.7720001133 178
29. 200 100 183
28 9...990.77700 78710. 180
20 10..200.90 20.2009777001778 7077
29. 7A 8D2.8C22.11111 175
29 80.88819100115
900.8800001120 170
Se14
PSNRR/SSIMY SSSIM/LPPISS LIPS
Urbun (N0
27. 77. 769.649942244
27. S/0. 70..66600222
28, J.1. N2..2224.0000 20.
26 1/0
26.5 26.5/0. 725/0 298
23 300 505/14437
27 6.0 840/0. 150
27 2/. 786/0.11111. 152
27 200 822/0. 158
27 57. 79800.99988 153
27.8 27.800 808/.110 159
28 3/0 845/0.151..
28 A/D 841.011.
139
28 50. 847
156
27
PSNR /SSIS/S PIPSS
28. 80500.0..0000 296
28 3..8 220 254
7.0 (32/0 270
27. 768/0. 360
24.5. 5.55 14.5.0..922.. (92/).9244444 534
20.20/08 440 236
788/0 243
28 4.0. 83 T/0. 244
28. 1O 80380 239
4/008 80.81188888 01..81300.22 226
83-40 239
28. 0ww.0366002200 222
(VD (37/)) 239
BSD
RI4KSR?
A.ComNSR
bicubic-+
SMFANe!
EIRLEN
Model
RT4KSR
bicubiic
ESPEN
ESPON
XLSR
XLSR
SAFMN
RLEN"
SPAN
SPAN
22

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Published as a conference paper at ICLR 2026
indicates fine- tuning our
DIV2K PSNRISSIMM /LPPSS. LPPPS.
www.6666600000 489
26 800 562/0 484
26. 53W0 572
551H5555555
28 SV. 13.500.70000 70470 391
29 69.00 10.60.75900. 7590 339
20..0221..000 F1AV 184
29 3/330 22200 380
220 NO 7290 376
0000 2360 $72
29 200 12 12 586
334
PO 10000 2AN0 14
75A0 375
27 27.300.96000
20 2
Table 11: Comparison of real time SR models on popular benchmarks (4x upsealing). Best results bold, second best underlined. datasel.
PSNR HSSM /LPIIS
23. 3.5/0047100.5 03242
23 518
23 23.6044663405555 553
23. 77.7733360000022
23 0400444500555
25 53.500548/4455
26 100.6132/04400
25. 1005566304335 43
25 30. 57305573/04333 432
26 16..00.80000000 425
59660960-223 -20
25 8/0 50356000443 434
26 6..606633//333 3V8
26 46.400661000 6000610000000 401
26. 60..0.
3M
REDS
26 26.20.59600
PSNR ASSII/SSIM-T ILSPSI LIPSS
1.0.52//0 503
25 2..20-44550055 52.1
3...444700 563
27. 110,562/04422 -21
27 w...86663100.555 588
27 1550588/0044000 407
27 77.600601//00000 A02
8/06 1/1 398
27 470400 AN0.578/0-40 409
28 2.0.617000000 382
A0..6100000. 35K
375
5...555000300 365
Sel14
PSNR P P/SSSMM/LLLL.. /LPIPS
23.7A0 48900.521
24 50000.122
23 8/1 493/0 589
23 23.9900-440 432/0.997
24 01-455205555
25 71.7711 58000 466
26 4/0.662////3441 341344
25. 595/50555555
26. (/0 608/0440
26 22.22006222 62000.42
632/0.44
26 0.000 597/0440
26 10. 68//0.33366
6/-40 369
26 327
27. 1. 69M90.32
Lrban100 (00
PSNR PSNR /SSMM /LPPSS
24. 24.00.00 ADDOO.SOSSS 575
24 D.. 11.99-4-330 40.30 589
(0. 43440 671
26 8... 26.800.5870 587/0 407
27. 17..50065990 (ww.65590....0 A50
27 61..6.2200 2/0 482
27. 1...06622 3.0.62220.441 47S
63..3320--000 468
27. NO (0300000000 492
27. 9.. 9T..0.66740 67..774 445
550.6320 4R1
BSD100
27.7.0
RIKKSR"
AsConvSR"
SMFANe!
SAPMN
EIRLEN
bicabic
ESPON
ESPEN
XLSR
XLSR
SAFMN
RL FN
KLEN"
SPAN
SPAN
23

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Published as a conference paper at ICLR 2026
MDTVSEA
0.
D. 0--9RC-0000
D. 1.503H0.000
0.50G-00012
0.5 0.5/220.00
0.5006-00017
0.500--0 -0
0.5055000077
0.50995..00
D. 0,52220 OIT
0.520
0.52320.000
Table 12: Ranking of real time SR methods with 95% confidence intervals. Best results bold. "T" indicates fine- tomingg N/A means that model does not have pretrained weights for specific upscaling factor.
BRQQ
0.4230001
0.4140011
0-4760001
0.454001
0.4660
0.486001
0.4740011
0.30HC011
0-4740001
0.8800O
0.46E001
0.4760011
0.524001
0.5
0.5260001
CLIP-100 1QA
1.366/100
0.3900002
044-0.002
0412002
0.400002
0.398100
04220024
0.35000
042-0
0.41000
04150004
0.2--002
512002
0,460000
0.4955002
MUSIQ
41:241.1
3908110
42. 42.1:1.
42.5+13
453512
47
439514
46611
436-14
46.111
43-4213
441+111
49.3
46.2.122
48.
4x Back
0.3823m008
0..022411000
0.278M01.044
0.215500:03
111910000
0.200100000
8.199991000
182
0,206600000
LIPIS
0.
SSIMI
02667a0 0012
0.4CH0 0044
0003000
0.5 002
CURSSEI
0250100
0.51
085359000
0,80000000
002
0.54510
038599 0012
PSAR
32.550015
32.050000
32.5500
32.8+0022
320460.3
32.0860.15
351/20015
33555013
33.4420.14
33.400012
1305-0.11
330660
38.3600117
31.3550.5
338220 12
32.47
33.51
Subj.
1.3250.14 14
1 14
1.4900 13
1.9660.15 IS
1.8600.1
2.31:0012 12
2.1 2618611114
2.1700
1.98-0 16
2.03
199100117 17
1.9360.15
4,3220.13 13
3.1-20.12 12
MDTVSEA
0.51
D. P.50DMH0000 OOS
0.505500000 OOS
0.521 0.521 00S
0.525 NOS
0.53750000 OOS
0.535
0.347280000 MOS
0.585500 008
0.550540 008
0.5525L0 009
0.5885H1
0.502500
NOA
NAA
0.520
NIA
0.5
FROQA
0.3360001
0.542001
0.58:0001
0.68-0.01
0.455001
0.55.001
0.5GeA01
NOA
055
NYA
0.55.00L
NAA
0.575001
0.55000
0.62:001
061:001
0.650001
0.748.111
0.740002
0.35.001
0.76.0.01
0.77500L
0.780002
CLIP-IQA
0.0020022
0,493002
0.53:0022
0444003
0455002
0.5.2002
0,52-102
033020222
0474002
0,14000
0.550022
0.548002
0.553002
0.574003
088:5002
0.660002
0.674003
0:681001
0.006000
0.713002
NNA
NAA
NIA
MUSIQ
52.521.2
58-7:13
40.5-1.2
50,2:10
56.911.2
001214
534.111
$6,41122
57.0c1.3
62-421.4
64.5115
07.81144
68 1:1.2
69 S:1.4
20.211.2
72.41122
NGA
NVA
N/A
2x Lrnc
LIPS
0214420.02
021
0U7UH0
000760.000 003
007280.004
007240.00 001
NAA
0.3000.0000
NYA
0158600007
NA
012261.00
007210.00 003
006600.00
0.033600002 000
0.20049.077
1.2244000100
0.1
0912200005
0.925-0000
0,72-0000
0,578-0000
0.00-00002
0.900
0.908=0000
0.97200002
0,94-00022
00925
0:905-00002
0:928
0,929 0,929,00000
0.900-200022
0.90 E50000
SSIM
NA
NAA
25 25
57 55:011
30 30
30.71-018
15
57 1030133
14
57 17.09511S
37.023U15
37 13
45
12.726012
37 11
37 12.0111
38 12:0 L3
PS
NON
NIA
NA
37
38
Suby.
19920.11
2.00211.12
2-
2.44-011
2.460.12
2.99.0.11
2.5TH1114
NOW
NVA
2.37.0.14
NIA
2.0930113 13
2.58.0.12
3.1380.1
3.87-0.13
4.330.15 15
1.50.0.14
Rea-ESS ESRGAN
Bicubic 2
NVIDIA VSR
Methed
RTAKSR
RUSKSR"
bisdii
ESPON
ESPONT
XLSS
XUSR
SNIFANGE
SMEANe
SATMN
SARMS
RLEN?
SPANT
EDRLEN
Swint
Omnn-S SR
BankVVR-
RVRT
24

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Published as a conference paper at ICLR 2026
20 DATASET PREVIEW
Figure 13 presents representative frames from the proposed StreamSR dataset. These samples illustrate the diversity of motion patterns, scene types, and compression characteristies captured in the data.
WONDERLANDS
TRANSIS OR
Figure 13: Example frames from the proposed StreamSR dataset.
25


Reviews:
[{"review_id": "GsV1oQ2Tql", "reviewer": "Reviewer_9P2C", "summary": "This paper addresses the challenge of enhancing video streaming quality via super-resolution (SR) in real-time. The authors note that standard SR models trained on datasets like DIV2K or Vimeo-90K struggle with heavily compressed streaming videos (e.g. YouTube content), which introduce artifacts (blockiness, blur, loss of detail) not represented in those datasets. To bridge this gap, the paper introduces StreamSR, a new dataset of 5,200 YouTube video clips (over 10 million frames) spanning diverse genres and resolutions (360p–1440p) representative of real streaming scenarios. They benchmark 11 state-of-the-art real-time SR models on StreamSR under two upscaling tracks (2× and 4×), using 7 image quality metrics (including PSNR, SSIM, LPIPS, CLIP-IQA, etc.) and a large-scale user study with 3,800+ participants. In addition, the authors propose a new efficient SR model called EfRLFN (Efficient Residual Lightweight Feature Network), which builds upon the prior RLFN architecture with targeted improvements. EfRLFN integrates Efficient Channel Attention (ECA) and hyperbolic tangent activations (tanh), along with a refined training strategy, to achieve better image quality and faster inference. Empirically, EfRLFN achieves state-of-the-art performance among real-time methods on the StreamSR benchmark, delivering favorable quality–complexity trade-offs. Notably, fine-tuning existing SR models on the StreamSR data yields significant performance gains that also generalize to standard benchmarks (the fine-tuned models show improved metrics on Set14, Urban100, DIV2K, etc.). Overall, the paper provides a comprehensive evaluation of real-time SR in streaming contexts: the StreamSR dataset, an improved SR model (EfRLFN), and thorough experiments demonstrating both objective improvements (e.g. higher PSNR/SSIM) and subjective gains (user preference overwhelmingly favoring EfRLFN outputs).", "questions": "See the weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novelty: The new StreamSR dataset is a valuable contribution, addressing a need for training/evaluating SR on real-world streaming content. It comprises 5.2K YouTube videos (25–30s clips) with aligned low/high-resolution pairs at 360p→1440p (4×) and 720p→1440p (2×) scales. Unlike prior SR datasets which often use pristine images synthetically downscaled (e.g. DIV2K, REDS), StreamSR provides naturally compressed low-resolution frames containing authentic streaming artifacts (from common codecs like VP9, H.264, AV1). This yields a broader quality range (MDTVSFA 0.41–0.61) more reflective of real YouTube content. The dataset spans diverse genres and content types, and the authors carefully filtered and clustered videos to ensure diversity in the test set. Overall, StreamSR is novel and well-constructed, better representing real streaming scenarios than previous SR data (which were shorter or lacked compression artifacts). This greatly enhances the relevance of benchmark results and will likely support future research in streaming video SR.\n\n2. Empirical Performance and Benchmarking Methodology: The experimental evaluation in this paper is comprehensive and rigorous. The authors benchmark 11 SR methods that are capable of real-time upscaling, ranging from classic baselines (Bicubic, ESPCN) to recent NTIRE winners (SMFANet, SPAN, RLFN) and even NVIDIA’s proprietary VSR solution. Each model is evaluated before and after fine-tuning on StreamSR, which provides insight into both out-of-the-box performance and achievable gains with adaptation. They report results on seven objective metrics, covering distortion-oriented measures (PSNR, SSIM), learned perceptual metric (LPIPS), and no-reference quality indices (e.g. CLIP-IQA and MUSIQ for human-aligned scoring), ensuring a well-rounded assessment of quality. Impressively, the paper also includes a user study with ~3,800 participants, collecting pairwise preference judgments. This large subjective test adds strong evidence of real-world efficacy, beyond just numbers. The results show EfRLFN’s outputs are overwhelmingly preferred by users over other methods (e.g. 77.4% of users favor EfRLFN over NVIDIA VSR in head-to-head comparisons). Such a combination of objective and subjective evaluation demonstrates empirical rigor. The benchmarking methodology is solid: the authors detail the evaluation protocol, use statistically significant comparisons (reporting confidence intervals), and even test generalization by applying StreamSR-trained models to standard datasets (Set14, Urban100, etc.). Notably, fine-tuning on StreamSR yields consistent improvements on these benchmarks, confirming the real-world data has broader benefits. Reproducibility is also addressed, the paper promises to release the dataset, code, and even the collected user study data, which should enable others to verify and build upon these results. Overall, the experiments are thorough and convincing, lending credibility to the paper’s claims.", "weaknesses": "1. Generalization Concerns: While StreamSR is a strong step toward real-world data, there are some questions about generalization. The dataset and experiments focus solely on YouTube videos and the prevalent codecs used there (VP9, H.264, AV1). This covers a large portion of online content, but it may not generalize to other domains or compression formats. For example, professional streaming or broadcast uses codecs like HEVC or VVC; these might produce different artifacts, and it’s unclear if models fine-tuned on StreamSR would handle them well. Similarly, the resolution range is up to 1440p, the benchmark does not evaluate 1080p→4K (2160p) upscaling, a common high-end scenario. The authors do show that models trained on StreamSR generalize to standard SR test sets, which is encouraging. However, the reverse direction (generalizing to content outside the YouTube distribution) remains uncertain. In future work, it would strengthen the contribution to expand codec and content coverage, e.g. include other video sources or higher-resolution tracks, to ensure the approach is robust beyond the exact domain it was trained on.\n\n2. Missing Ablations and Comparisons: The authors conducted several ablation studies (activation functions, loss functions, etc.), but a few comparisons are either missing or left to supplemental material. For instance, an ablation on the composite loss would be useful to see how each component (Charbonnier, VGG perceptual, Sobel) contributes to final quality, the paper mentions this composite greatly helped training stability, so showing a breakdown of its impact would solidify that claim. Similarly, since EfRLFN is built upon RLFN, it would be enlightening to have a direct comparison of training with vs. without the new StreamSR data for that model: e.g. how much does fine-tuning RLFN on StreamSR improve its performance on compressed videos (the authors do include this result) and on standard benchmarks (they report gains). Such comparisons are partly given (Table 2 and discussion), but a more detailed ablation could isolate the effect of the dataset itself on a model’s performance. Another missing aspect is any comparison (even qualitative) to video-specific SR methods that leverage temporal redundancy. While most such methods are too slow for real-time, it would be interesting to see if, for example, a two-frame fusion approach could outperform single-frame EfRLFN at the cost of some speed, this could inspire future research on reaching a better quality/speed middle ground. Lastly, the main paper emphasizes 2× upscaling results; the 4× track results are deferred to the appendix. It might improve the paper to at least summarize the 4× findings in the core text, since 4× (360p→1440p) is a significant challenge and presumably where EfRLFN’s advantages also shine. In conclusion, additional ablations (loss function components, dataset contribution) and broader comparisons (to non-real-time or multi-frame methods) would further strengthen the work. These omissions do not undermine the existing results, but addressing them could provide a fuller picture and preempt questions from readers. The authors are encouraged to incorporate such analyses either in a revision or future follow-up, to solidify the paper’s conclusions and explore the boundaries of the proposed approach.\n\n3. Methodological Clarity and Experimental Limitations: For the most part the paper is well-written and thorough, but a few methodological points could be clearer. The novelty of the EfRLFN model might be seen as somewhat incremental, it is essentially an improved variant of RLFN with known techniques (ECA attention, tanh activation) adopted from prior works. The paper does justify these choices well and supports them with ablations, but some readers may feel the model innovation is an optimization of existing components rather than a fundamentally new architecture. This is not a severe issue (pragmatic improvements are valuable for real-time tasks), yet highlighting this context is important for clarity. In terms of experiments, one limitation is that the authors restricted comparisons to real-time models, excluding stronger but slower SR networks (like SwinIR, Real-ESRGAN) from quantitative evaluation. Their reasoning is valid, those models fall far short of real-time speeds, but it does slightly limit the perspective on the ultimate achievable quality. Including at least a reference comparison to a top-performing offline model (even if only in an appendix or figure) could help quantify the quality gap between real-time and non-real-time SR. Another minor clarity point is the lack of detail in the main paper about the user study protocol (though presumably in the supplement): understanding how pairs were presented or how scores were aggregated would help gauge reliability. Finally, the work focuses on single-frame SR; there is no exploration of multi-frame/video SR algorithms that exploit temporal information. While this choice is appropriate for real-time efficiency, it’s an implicit limitation, methods that use neighboring frames (even in a limited way) might recover details or reduce flicker that single-frame methods cannot. Clarifying this as a conscious scope choice (and perhaps discussing the potential of efficient multi-frame SR in the future) would strengthen the methodology section. In summary, the paper could be more explicit about these limitations and choices, to enhance transparency and guide future extensions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MGV0ykkULK", "reviewer": "Reviewer_yoXt", "summary": "This paper addresses real-time super-resolution for compressed streaming videos. The authors propose **StreamSR**, a large-scale dataset of real YouTube videos with compression artifacts, and **EfRLFN**, an efficient network designed for high-quality, low-latency upsampling. A comprehensive benchmark on 11 methods, including objective and subjective evaluations, demonstrates the effectiveness of the propsed approach.", "questions": "see weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper addresses a timely and practical problem: enhancing low-quality compressed videos in real-world streaming scenarios. By introducing both a new dataset and benchmark, it highlights the limitations of current SR methods under realistic conditions.\n2. The StreamSR dataset fills an important gap by providing a large-scale collection of real YouTube videos with natural compression artifacts, offering a more realistic evaluation platform than synthetic benchmarks.\n3. EfRLFN achieves competitive performance among real-time SR models, with strong results on the proposed dataset and favorable efficiency-quality trade-offs, validated through extensive experiments.", "weaknesses": "1. The paper focuses on real-time SR for compressed streaming videos, but the related work primarily discusses general real-time SR methods, with little discussion of existing compressed VSR approaches. Important works in this domain—such as *Learning Degradation-Robust Spatiotemporal Frequency-Transformer for Video Super-Resolution*—are not adequately reviewed. A dedicated discussion on compressed VSR literature is needed.\n2. The main claimed contribution is the StreamSR dataset, yet no sample frames or metadata (e.g., compression settings like bitrates, codecs, and resolution scaling methods) are provided in the paper or supplementary material. Including visual examples and detailed encoding information would help reviewers assess the dataset’s quality and realism.\n3. The proposed EfRLFN lacks clear architectural novelty for handling compression artifacts. The design appears to be a refined version of RLFN without explicit mechanisms to address compression distortions (e.g., blocking, blurring). The performance gain may stem largely from training on the new dataset rather than model innovation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "G1iz2vLaeu", "reviewer": "Reviewer_2ofZ", "summary": "This paper identifies a critical gap in existing super-resolution research: the lack of datasets that realistically model the compression artifacts found in real-world video streaming. To address this, the authors make several contributions. First, they introduce StreamSR, a new, large-scale dataset of 5,200 videos sourced from YouTube, which contains authentic compression artifacts. Second, they conduct a comprehensive benchmark of 11 state-of-the-art (SOTA) real-time SR models on this new dataset, notably including a large-scale subjective user study with over 3,800 participants. Third, they propose EfRLFN, a lightweight SR model based on RLFN, which incorporates modifications like Efficient Channel Attention (ECA) and a tanh activation function to achieve a strong balance of quality and runtime performance. The authors show that their model, as well as other models fine-tuned on StreamSR, achieve significant performance gains.", "questions": "Reframing of Contributions: Given that the paper's most significant and lasting contribution is arguably the comprehensive StreamSR dataset and the large-scale benchmark, would the authors consider reframing the Abstract and Introduction? Currently, the EfRLFN model is presented as a primary contribution on par with the dataset. However, as noted, the model's architectural novelty is limited, and similar designs are common in NTIRE reports. Perhaps positioning the dataset and benchmark as the central contribution, with EfRLFN serving as a strong new baseline developed for this benchmark, would more accurately reflect the paper's impact.   \n\nClarification of Scope (SISR vs. VSR): The paper's motivation is \"video streaming\" and \"video content\". However, the proposed EfRLFN model is explicitly a single-image SR (SISR) method (\"we focus on image SR for our approach\"). This is a valid and common strategy for real-time applications, but it does not leverage temporal information. Could the authors please clarify this distinction more prominently in the Abstract and Introduction? This would help set reader expectations, as \"video super-resolution\" often implies temporal (VSR) methods.   \nDeployment Performance: Following up on the weakness regarding efficiency claims: I would be very willing to reconsider my score if the authors could provide even preliminary data on deployment. For instance, what is the inference speed, throughput, or power consumption of EfRLFN and a key competitor (like SPAN or RLFN) when exported (e.g., to ONNX) and run on an inference engine like TensorRT or OpenVINO? This would provide much stronger evidence for the model's practical, \"hardware-friendly\" efficiency beyond the PyTorch runtime. If this is not feasible, I strongly suggest adding a discussion of this limitation and positioning platform-specific optimization as future work.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Dataset Significance and Quality: The paper's most valuable contribution is the StreamSR dataset. The authors correctly argue that popular datasets like DIV2K and Vimeo90K do not adequately represent the challenges of real-world streaming media. The effort to collect, filter, and curate a 5,200-video dataset with associated low- and high-resolution pairs from a streaming source is substantial and addresses a clear need in the community .\n\nComprehensive Benchmarking: The second major strength is the systematic and thorough benchmarking of 11 real-time SR methods. This evaluation is rigorous, employing 7 different objective metrics and, most importantly, a large-scale subjective study.\nLarge-Scale Subjective Study: The inclusion of a subjective evaluation with 3,822 participants and over 37,000 valid responses is highly commendable. This provides a much-needed perceptual grounding for the objective metrics, and the community is well-aware of the significant time and financial cost required to obtain such data. The paper's finding that fine-tuning on StreamSR significantly improves the performance of existing models like SPAN and RLFN strongly validates the dataset's utility and \"real-world\" nature.", "weaknesses": "Limited Architectural Novelty: The primary weakness is the limited originality of the proposed EfRLFN model. The architecture appears to be a thoughtful and effective combination of existing lightweight techniques rather than a novel design. The key modifications—such as replacing ESA with ECA and using a tanh activation—are well-motivated by prior work (e.g., SPAN ) and common practices from recent NTIRE challenges. While the ablation studies (e.g., Table 3, Figure 5(b)) are detailed and confirm the authors' design choices, they largely reinforce existing knowledge (e.g., the benefit of odd symmetric activation functions) rather than providing fundamentally new insights for the field.\n\nInsufficient Evidence for \"Hardware-Friendly\" Claims: The paper repeatedly emphasizes that EfRLFN's design is \"efficient\"  and \"hardware-friendly\". However, these claims are supported only by FPS measurements from PyTorch on an NVIDIA RTX 2080 GPU. This benchmark is a useful proxy but does not represent the performance in a true deployment scenario. Real-world applications typically involve inference engines (e.g., TensorRT, OpenVINO) and further optimizations like model quantization, operator fusion, and platform-specific kernel tuning. I understand that a full deployment analysis is a complex task, but the \"hardware-friendly\" claims are not sufficiently substantiated by the current experiments. At a minimum, this limitation should be discussed in the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NSffJ7eOMl", "reviewer": "Reviewer_LP9y", "summary": "The paper introduces StreamSR, a large-scale dataset of 5,200 compressed YouTube videos for realistic benchmarking of real-time super-resolution. It also proposes EfRLFN, an efficient model using tanh activations, channel attention, and a composite loss for better quality-speed trade-offs. Extensive experiments and a large user study show EfRLFN achieves the best performance among real-time SR models and that fine-tuning on StreamSR boosts other models’ results.", "questions": "Refer to above.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. StreamSR focuses on real-world compressed videos instead of the clean, synthetic data used in most existing benchmarks.\n\n2. The paper includes both objective metrics and a large-scale subjective user study, which adds credibility to the results.\n\n3. In addition to the compressed dataset, this paper proposes EfRLFN, an efficient real-time super-resolution model designed to further enhance both visual quality and inference speed.", "weaknesses": "1. The proposed EfRLFN model is a very incremental change over the existing RLFN . It swaps an attention block (ESA for ECA) and an activation function (ReLU for tanh).\n\n2. The paper frames the problem as super-resolution for streaming content (i.e., video), but the proposed model is a single-image SR model applied frame-by-frame. This approach completely ignores temporal information, which is a critical component of video processing.\n\nDetailed comments:\n 1. My main concern is the disconnect between the problem -- video streaming-- and the proposed solution, single-image super-resolution (SR). Processing each video frame independently can lead to temporal artifacts such as flickering or popping because the model lacks context from previous or future frames. A true video SR model would utilize this temporal information to maintain consistency. The paper justifies its approach by citing that existing VSR models are too slow, which is valid, but it does not address potential quality trade-offs of their method. When applying EfRLFN to video, does it introduce temporal instability compared to models like NVIDIA's VSR—which, although proprietary, likely incorporates temporal awareness?\n\n 2.Furthermore, the paper's own results show that a large part of the performance gain comes from the data, not the model. When SPAN and RLFN are fine-tuned on StreamSR, their performance jumps dramatically. This is great validation for the dataset, but it makes the EfRLFN model itself seem less essential. It seems the paper's primary finding is \"training on real-world compressed video is better,\" which, while true, is not a huge surprise", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)