QBasicVSR: Temporal Awareness Adaptation Quantization for Video Super-Resolution
Authors: Zhenwei Zhang
Fanhua Shang
Hongying Liu
Liang Wan
Wei Feng
Yanming Hui
Venue: NeurIPS 2025 poster ()
Keywords: video super resolution (VSR)
post training quantization (PTQ)
efficient VSR
Primary area: deep_learning

Abstract: While model quantization has become pivotal for deploying super-resolution (SR) networks on mobile devices, existing works focus on quantization methods only for image super-resolution. Different from image SR quantization, the temporal error propagation, shared temporal parameterization, and temporal metric mismatch significantly degrade the quantization performance of a video SR model. To address these issues, we propose the first quantization method, QBasicVSR, for video super-resolution. A novel temporal awareness adaptation post-training quantization (PTQ) framework for video super-resolution with the flow-gradient video bit adaptation and temporal shared layer bit adaptation is presented. Moreover, we put forward a novel fine-tuning method for VSR with the supervision of the full-precision model. Our method achieves extraordinary performance with state-of-the-art efficient VSR approaches, delivering up to $\times$200 faster processing speed while utilizing only 1/8 of the GPU resources. Additionally, extensive experiments demonstrate that the proposed method significantly outperforms existing PTQ algorithms on various datasets. For instance, it attains a 2.53 dB increase on the UDM10 benchmark when quantizing BasicVSR to 4-bit with 100 unlabeled video clips. The code and models will be released on GitHub.

SeasiVSR:: Temporal Awareness Adaptation Quantization for Video Super-Resolution
Zhenwei Zhang', Fanhua Shang',
Hongying Li W
Wei Feng', Yanming Hui!
School of Computer Science and Technology. Tianjin University "Medical School, Tianjin University *Peng Cheng Lab, Shenzhen *School Computer Software, Tianjin University
(microsoftzzv, fhahang, hyliu2009, lwan, wfeng, yahui)@tju.een edu. cn
Abstract
While model quantization has become pivotal for deploying super-resolution (SR) networks on mobile devices, existing works focus on quantization methods only for image super-resolution. Different from image SR quantization, the temporal error propagation, shared temporal parameterization, and temporal metric mis- match significantly degrade the quantization performance of video SR model. To address these issues, we propose the first quantization method, QBasic for video super-resolution. A novel temporal awareness adaptation post-training quantization (PTQ) framework for video super-resolution with the flow-gradient video bit adaptation and temporal shared layer bit adaptation presented. More- over, we put forward novel fine-tuning method for VSR with the supervision of the full-precision model. Our method achieves extraordinary performance with saate-of-the-ase art efficient VSR approaches, delivering up to x200 faster process- ing speed while utilizing only 1/8 of the GPU resources. Additionally, extensive experiments demonstrate that the proposed method significantly outperforms ex- isting PTQ algorithms on various datasets. For instance, it attains a 2.53 dB in- crease on the UDM10 benchmark when quantizing BasicVSR to 4-bit with 100 unlabeled video clips. The code and models will be released on GitHub.
Introduction
Super-resolution (SR) is a classic low-level computer vision task focused on reconstructing high- resolution image (s) from their low-resolution (LR) counterparts. In video scenarios. extends to aggregating spatiotemporal information from multiple misaligned frames within sequence, re- quiring motion compensation and frame alignment to achieve coherent detail enhancement across the temporal domain. Despite demonstrating impressive performance, state-of-the-art video super- resolution (VSR) networks [3, 28, 29] face deployment challenges devices due to their high computational and memory demands. Furthermore, the growing industry demand for real-time large-scale VSR (e.g., 4K video streaming) introduces quadratically increasing computational complexity. To enhance computational efficiency in VSR, researchers have explored various efficient methods, such reparameterization [21] and network pruning [25, 42, 46), which aim to reduce model size or simplify network structures. In parallel. model quantization has proven be another powerful efficiency-dii ddiven technique [14. 26, 37, 38, 47]. by leveraging low-precision arithmetic to reduce latency, memory footprint, and consumption cost on AI accelerators [6, 41]. However, despite its advantages, quantization has not yet been explored in the domain of VSR.
essource-constrainee edge
*Corresponding authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Although existing efficient VSR methods have achieved notable progress in reducing parameter sizes and improving inference speed, their critical limitation stems from reliance on 32- floating-point (FP) operations. This high computational precision inherently restricts their ability to fully exploit the capabilities of modern AI accelerators, which are optimized for low bit-width computations. Furthermore, these efficient methods in VSR necessitate processing entire datasets during training, resulting in prohibitively expensive computational overhead that persists despite their architectural optimizations. As shown in Table 4, the SOTA efficient Basic method [42] requires training 303,380 iterations with a batch size of 8, which takes approximately 15 days on NVIDIA A6000 GPUs. Quantization has emerged as critical, efficient strategy, systematically replacing 32-bit FP representations in network parameters and activation tensors with reduced-precitee arithmetic,, thereby achieving up to 4x memory footprint compression and 2-3x latency reduction in hardware- accelerated inference engines.
Current mainstream quantization methods can be broadly categorized into two paradigms [11]: quantization-awar training (QAT) approaches that require complete training datasets for end-to- end model retraining, and post-training quantization (PTQ) methods that directly leverage pre- trained models with only a few unlabeled calibration samples. While image SR quantization meth- ods [14, 16, 26, 37, 38, 47] have achieved advanced performance with minimal degradation in accu- racy, there are seldom works on quantization for VSR. When transitioning from image to VSR, three fundamental challenges emerge [3, 4, 28, 29]= : (1) Temporal error propagation: the recurrent archi- tecture, which is adopted in almost all VSR models, accumulates quantization errors across inter- frame dependencies; (2) Shared temporal parameterization: tright-sharing in VSR across time steps introduces compounded quantization effects absent in single image processing; (3) Temporal metric mismatch: conventional image quality assessments fail capture temporal consistency degradation. Therefore, existing quantization methods developed for image SR can not be directly transferred to VSR tasks. Crucially, even when attempting to adapt QAT to VSR, significant barriers persist. While QAT could theoretically recover quantized model performance, it demands full retraining with the same iterations as original FP models. Each training step incurs exacerbated GPU memory and prolonged duration due to gradient accumulation through recurrent units and quantization-awee gradient calculations. Given that VSR training already requires orders of magnitude more computa- tion than image SR, such additional overhead makes QAT-based retraining unrealistic for practical deployment, particularly in rensurce-constrained scenarios. Therefore, we concentrate on the PTQ methods in this paper.
To address the issues above, we propose the first VSR quantization method for accurate model in this paper. Our framework decouples the adaptive bit allocation mechanisms between video streams and parameter-shared layers, allowing independent optimization of bit-width adaptation reduce the heavy computation. For video streams, we propose a flow-gradient video bit adaptation strategy driven by spatiotemporal complexity metric. Videos exceeding the upper threshold are assigned a positive factor that increases bit-width allocation. Calibration datasets are then utilized to re- fine the threshold through backward propagation. Subsequently. by exploiting the spatiotemporal correlations inherent in video data, a temporal shared layer bit adaptation module is presented to evaluate the quantization sensitivity of each layer through temporal-aware analysis. This analysis captures both intra-frame features and inter-frame temporal dependencies, ultimately determining the bit factors for the weight-sharing layers. The layer-wise sensitivity calculated by processing the calibration datasets with the pre-trained FP network. Layers exhibiting sensitivity values exceed- ing the upper threshold are identified as critical components and assigned positive adaptation factors. These layer-specific adaptation factors undergo further calibration through direct fine-tuning using the same calibration datasets, enabling precise adjustment of quantization parameters while pre- serving temporal coherence across video frames. This dual-optimization architecture, strategically combining temporal-awe -awre bit allocation (for both video and VSR network layers) with calibration- driven sensitivity refinement, establishes optimized quantization scheme designs that holistically balance spatial-textural details, temporal evolution patterns, and layer-specific representation re- quirements throughout the video restoration pipeline. Compared to traditional VSR methods, which rely on complete labeled ground truth (GT), our method achieves rapid quantization with unprece- dented efficiency, completing the entire process using only a small collection of unlabeled video clips. The contributions of this work are summarized as follows:
(1) To the best of our knowledge, this is the first work that explicitly addresses model quantization for video super- rupertresolution task.
2

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(2) We propose novel dual-optimizttion framework with a flow- now-gradient video bit adaptation strat- egy guided by spatiotemporal complexity metrics, coupled with temporal shared layer bit adapta- tion module that dynamically coordinates layer-wise bit-width distribution.
(3) Moreover, we present novel fine-tuning method for VSR tasks with the supervision of the full-precision model. Our method outperforms existing efficient VSR approaches. Furthermore, our method outperforms existing PTQ techniques by substantial margin.
(4) To promote collaborative studies and industrial deployments, we also provide QBasicVSR, novel quantization VSR library. This new library includes the complete implementation, such as training protocols, quantization aware modules, and pre-trained model weights.
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(c) The proposed TS-LBA and FG-VBA modules
Figure 1: Overview of the proposed temporal awareness adaptation quantization for VSR. Parame- ters are shared within green and blue blocks separately. (a) The frames 4, 4z and 4++1 represent consccutive LR video frames. F' and B denote the forward and backward modules. (b) During inference, the TS-LBA module is fixed, and the FG-VBA module is adapted according to the video complexity. (c) The TS-LBA and FG-VBA modules dynamically modulate the bit-widths of layers.
2 Related Work
Model quantization has emerged as a critical technique for compressing and accelerating deep neu- ral networks, garnering significant research attention and achieving ubiquitous adoption in modern Al systems due to its effectiveness enabling efficient deployment across resource-constrainee platforms. PAMS [26] addresses dynamic activation variations by adaptively updating quantization ranges to match their statistical characteristics. Consequently, DDTB [47] successfully pushes the compression boundary ultra-low 4-bit precision without catastrophic accuracy degradation by de- veloping test-time quantization range adaptation through dynamic parameterization. Furthermore, recent works, CADyQ [14] and CABM [37], propose adaptive quantization frameworks employing content-aware bit- widt prediction modules that dynamically adjust quantization levels based on in- put image characteristics. However, these methods all require QAT with a complete dataset and often incur even higher computational costs than training full-precision models, particularly problematic for videos where processing high-dimensional temporal data makes such retraining procedures pro- hibitively expensive in both time and computational resources.
3

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To overcome these limitations, PTQ enables efficient model compression without retraining by cal- ibrating quantization parameters using only a few unlabeled samples. Tu et al. [38] proposed the first PTQ method for image SR, which introduces the density-based dual clipping to cut off the out- liers by analyzing the asymmetric bounds of activations. To promote adaptive inference, Cheeun et al. [16] proposed an adaptive quantization method, which achieves competitive performance with the previous adaptive quantization methods. However, these methods are specifically designed for image SR and cannot be directly applied to video models. To this end, we propose video quantiza- tion framework that requires only a few unlabeled video clips to determine quantization parameters automatically.
3 Methodology
Our method, illustrated in Fig. proposes a novel adaptive quantization method, temporal aware- ness adaptation quantization for VSR. The bit allocation strategy comprises two adaptive modules: a temporal shared layer bit adaptation, which dynamically assigns layer-wise bit-widths based on spatial and temporal sensitivities, and flow-gradient video bit adaptation, which globally adjusts bit-widths guided by a unified spatiotemporal complexity metric. After the adaptive bit-width con- figurations are determined by the two modules and finalized during the calibration phase, we design a novel fine-tuning step. This fine-tuning further optimizes the quantization parameters, significantly enhancing reconstruction accuracy while maintaining computational efficiency. During inference, the layer-wise bit-widths remain fixed, while the bit-widths are adaptively adjusted based on the input video complexity through the flow-gradien video bit adaptation module.
3.1 Preliminaries
We briefly introduce the quantization approach, focusing on asymmetric quantization for activations, as is standard practice for SR networks due to their asymmetric activation distributions [14, 15, 37, 47]. Given a floating-pont point tensor x' with bit-width b. the quantization process is formulated as:
Tee Clamp ar, 1, u) mminmmx((;; u):
3e 1,
" u-1 1
Tint
(1)
5
2
1
Fg Tint S 11
where and denote the lower and upper clipping thresholds, b the quantization bit-width, and S represents the scaling factor that maps the clamped tensor x. (confined to [1. u)) to 2 discrete levels. The operator implements nearest-integer rounding, while Fint corresponds friendly integer representation. For weights, we enforce symmetry quantizer by setting = u.
the hardware-
3.2
Frad-Gta Video Bit Adaptation
Our goal is to determine universal flow-gradien synergized adaptation factor, which dynamically adjusts the uniform bit-width for all layers based on spatiotemporal complexity characteristics of input video streams. To achieve this, we design joint gradient-flov complexity metric that models spatial texture richness and temporal motion dynamics. Below, we formalize the components of this metric and their mathematical definitions.
For each frame 4 video stream of T frames, where E {L..... T). we compute spatial complexity by aggregating gradient magnitudes across the frame. Let Ω = {(i.j) 0 < < H,O < j W) denote the set of all pixel coordinates in the t-th frame with height H and width W. The spatial gradient (i. j) at pixel (i.j) j) is defined as vector containing horizontal and vertical derivatives, computed via finite differences:
Spatial Gradient Analysis:
04 Or (i, 1. + 1,j) (ii.)),
04
(i.j) [(ij+ I.(i.j). By
The spatial complexity S, for frame / is then calculated as the mean -norm of gradients across all pixels, scaled by 10 to amplify small magnitudes:
103
S = Ω
Σ (
(3)
(U))R

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where (I,)))) 014 (i,J).
01 (i.5) [ H x W, denotes the total number of pixels, and By
||- lI represents the -nrrm. This method emphasizes high-frequency spatial details (e.g., edges and textures), critical indicators of visual complexity in videos.
Temporal Flow-Field Dynamics: To holistically model video complexity, we characterize temporal motion dynamies through bidirectional flow-field analysis. Let B Fb denote the set of for- ward (t + t++ 1) and backward (++1 + t) flow fields estimated by SPyNet [34], where each vector F(i.j) (u(i,j), v(t.j) E 222 represents horizontal and vertical displacements of pixel (i. The temporal complexity is decomposed into complementary magnitude and consistency components. The flow magnitude term quantifies average motion intensity across bidirectional flows:
T magnitude,
Σ [i, FEB, (8,))
(4)
[&
122
where [Bel 2 accounts for bidirectional flows, [ H x X is the pixel count, and |1-112 computes the f--norm of displacement vectors. Beyond motion magnitude, we quantify flow consistency by measuring the spatial smoothness of flow vectors. For each flow field F B,, compute the Jacobian matrix Ji) E R=2x at pixel (i, defined as:
the dyu
Jp(i. =
(5)
By
where partial derivatives are approximated via finite differences:
Ou
du
Or (i. nli + 1, u(i,s), By
(i.j) u(i, + -))((,))
and similarly for t-components. The element-wise & normr || (1. () aggregates absolute gra- dients of the flow field. The consistency term aggregates the element-wise 4-norm of Jacobians across all flows:
T consistency,
Σ
Σ
(1
B, Q
FEB, (i.))R
Finally, the temporal complexity T, combines the magnitude and consistency terms through a weighted summation:
T. = T=Tgnnggiddd. + 7 Tonssisency,.
(8)
where y is a balancing coefficient that determines the relative importance of motion magnitude versus motion irregularity. To holistically model video complexity and bridge spatial texture details with temporal motion dynamics, we introduce a spatiotemporal complexity metric Cvideo which unifies spatial and temporal components into a single guiding signal for bit adaptation. The overall dow-gradient complexity metric is defined as:
T Σ Se+ T
Ovideo
T
Σ (maagiiude: + y Teonsistency ,,,,
where St represents the spatial complexity of the t-th frame (defined in Equation (3)), T Tiagnitude , and T Temmsistency. denote the temporal magnitude and consistency terms at time t (defined in Equations (4) and (7), respectively. The parameter À balances the relative contributions of spatial texture rich- ness and temporal motion dynamics. Finally, we design a flow- flow-grade www-rradiett driven bit-width adaptation module that maps Cviteo global bit-width factor. This metric integrates spatial texture details and temporal motion dynamics, enabling a unified characterization of video complexity. During inference, the video-to-bit allocation function converts the computed complexity Cvideo into a global bit-width adaptation factor b,.. which dynamically adjusts the layer-wise bit-widths of the VSR net- work. The allocation mechanism operates as follows: if Cvidco exceeds an upper threshold u,26. the bit-width factor b, is set to +1, globally increasing the bit-widths to preserve fidelity in scenes with intricate textures or intense motion; if Crideo falls below a lower threshold (.26, b, is set to -1, reducing bit-widths to optimize computational efficiency for simpler videos; for values within the thresholds, by remains O, maintaining the default quantization configuration. The thresholds 4.26 and My26 are initialized using percentile- based statistics from a small set of calibration video clips. Specifically, 1,28 corresponds to the py-th percentile of the video complexity distribution across calibration data, while 2,26 is set to the (100- Pv )-t percentile. While the statistical thresholds offer a practical baseline for determining video-adaptive bit-width allocations, we achieve superior optimization performances through threshold fine-tuning.
5

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3.3 Temporal Shared Layer Bit Adaptation
is widely acknowledged that different layers in neural networks exhibit varying sensitivity to quantization [7, 9, 35, 39]. Layers with lower sensitivity can tolerate aggressive bit-width reduc- tion without significant accuracy degradation, enabling better computational efficiency. To address the unique challenges of VSR networks, where parameters are shared across frames, we propose temporal shared layer bit adaptation module. For each convolutional layer k; we define two comple- mentary sensitivity metrics that jointly guide layer-wise bit-width adaptation. Given the input video sequences with T frames, we stack activations into A*
stacked E RRTxBCC*H** (B: batch size, C: activation
channels, H x W: spatial dimensions), the spatial sensitivity s variability through channel-wise standard deviations:
Squce quantifies
space Er
[oe ( (Rsisceedd...b..h..)))))
(10)
where k,,,,,, denotes averaging over time step t. batch index b and spatial positions (h, w). oe(-) computes the standard deviation across the channel dimension (dim=2). This captures spatial texture complexity: higher values indicate regions with edges fine details requiring higher bit-widths. Moreover, the temporal sensitivity s Stepp models inter-frame dynamics as:
temp Es,,ch,, for (R
(11)
where Fa averages over batch b, channels e, and spatial coordinates (h, w). ax(+) calculates the unbiased standard deviation along the temporal dimension (dim=0). Elevated Stemp reflects dynamic regions with significant motion between frames. The layer-wise bit adaptation factor 1t, is determined by joint thresholding:
-1, 1, if (ppaee st stemp) E [0, Aspace] x [0, dtempl = +1, (s spacee stemp) E Muspace; 00) x [Rreepp 0c)
69
(12)
0, otherwise
where spatial thresholds (apace and Uspace initialized as the Pspace-th and (100 Pspace )-th per- centiles of spatial sensitivity values { } from the calibration dataset, while temporal thresholds hemp and Unemp correspond to the Phemp- th and (100- Phemp)-th percentiles of temporal sensitivi- ties {s }. The Cartesian product [0. Lsaace] ] x 4wmp) identifies stable regions where sensitiv- ities fall below lower percentiles, enabling bit reduction without quality degradation. Conversely, [Uspace: oo) [Mempp oc) defines regions where both spatial texture complexity and temporal motion dynamics exceed upper percentiles, necessitating increased bit-widths.
3.4 Calibration
For initializing the clipping ranges of quantized activations, we collect minimum and maximum activation statistics [20] by forwarding calibration video clips through the FP network, which are smoothed using the exponential moving average (EMA) to stabilize the lower and upper bounds of each quantizer. Our framework decouples the adaptive bit allocation mechanisms between input video streams and parameter-shared layers. Notably, the calibration phase determines the layer- wise factors { which are pre-determined and fixed during inference, leaving only the flow- gradient video bit adaptation module active. After (b activation clipping ranges using the OMSE [8] criterion. In short, the bit-width for quantizing video stream V in layer le is dynamically adjusted through a combination a flow How-gradien video bit adaptation factor and temporal shared layer bit adaptation factor, expressed as:
K are determined, we further refine the
Unnce + by bū. b
(13)
where by denotes the bit-width for video stream V in layer k bruse is the baseline bit-width, by accounts for video complexity, and by reflects the quantization sensitivity of each layer.
3.5 Fine-tuning
While calibration provides a reasonable starting point for the quantized VSR network, we further fine-tune the bit adaptation modules through calibration data to achieve better adaptation. Since

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layer-wise bit adaptation factors {b]] k= are pre-configured and remain static during inference, optimize them directly through gradient descent. In contrast, the video-wise bit adaptation factor by is dynamically determined during inference based on video complexity. We therefore fine-tune the flow-gradient video bit adaptation module, which is parameterized by threshold values 2,26 and uy2s. To overcome the non-diiferentiability of the thresholding operation in this module, we employ a tanh-based gradient approximation during backpropagation, inspired by the approach in [12]. Si- multaneously, we optimize the clipping boundaries for activation quantization (la: ua) and weight quantization (~uw, ). For the non con-differentiblee rounding operation in the quantizer, we uti- lize the Braight-Thhough Estimator (STE) [1], maintaining the gradient flow by approximating the rounding function as an identity operator during backward passes while preserving its exact behav- ior in the forward path. During optimization, the super-resolution network's weights remain fixed to maintain the pre-trained knowledge of the full-precision model P. Inspired by the Charbonnier loss [5] in VSR, we design the reconstruction loss as follows:
Spix T E VIIP(1,A-- Q(1 - +
(14)
where T denotes the number of input video framess 1, represents the i-th low-resolution frame, P(.) and Q(-) correspond the full-precision (FP) and quantized networks respectively, || llll is the L2 norm measuring feature discrepancies, and e is a small positive constant introduced to ensure numerical stability and maintain differentiability of the loss function, particularly when Cux ap- proaches zero. For feature- supervision, we extend the pixel transfer loss [38] to video domains by incorporating temporal dimension awareness. Given intermediate features from the FP network P and quantized network Q, we first perform layer-wise L2 normalization across T consecutive frames, and then we calculate the mean square error of these two feature maps across all the blocks and temporal frames. The pixel transfer loss is formulated as follows:
T
K
Fi
Fe
Lik1 T
(15)
L
where T denotes the number of temporal frames, K' represents the total number of feature layers, Fip E x and Fi,k E H denote the k-th layer features for the i-th frame from P and Q respectively, with (Cx, HA. W&) indicating the channel depth and spatial dimensions of the k-thh layer outputs. The L2 normalization ensures scale-invariant feature comparison, while the temporal summation over T frames explicitly models frer-frrme relationships in video sequences. Such supervision paradigm operates solely on low-resolution inputs, eliminating the dependency on high-resolution ground-truth references. Finally, we can get the total loss:
Liotal = Lpix + Caks,
(16)
with Ask balancing frame-wise reconstruction and temporal feature consistency in recurrent video networks. This formulation enables optimization of quantization parameters across both spatial and temporal dimensions, effectively suppressing error propagation in cyclic computation while preserv- ing video texture details through adaptive adjustment. The quantization parameters are iteratively optimized through three consecutive steps after the calibration phase: initially updating weight clip- ping ranges {u 1== with activation and bit adaptation module parameters frozen, subsequently re- 1 fining activation clipping ranges {1%, " ( k=1. and finally adjusting bit adaptive module parameters K l.2b, 2,2 {b} === Each step exclusively optimizes the parameter subset while strictly maintaining the others in fixed states, completing the fine-tuning within only three epochs.
4 Experiments
4.1 Implementation Details
We build the calibration datasets by randomly sampling 100 LR video clips from the REDS [33]
and Vimeo-90K [44]. For REDS, we use REDS4, containing four clips, as our test set. In addition,
we utilize Vid4 [30], UDMI0 [45], and Vimeo-90K-T [44] as test sets along with Vimeo-90K.
We train and test models with 4x downsampling using two degradations, bicubic (BI) and blur
7

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downsampling (BD), as BasicVSR [3] did. The calibration datasets calibrate and fine-tune our bit adaptation modules and the quantization ranges. The quantization range for activations is initialized using MinMax [20], and weights OMSE [8]. The parameters 7 and À in the flow- now-gradient complexity metric are set to 200 and 10. The hyperparameters for calibrating the bit adaptation modules, pv, Pepace. and Ptemp are set to 10, 30 and 30. The constant in the reconstruction loss is set to 10-6. The parameter Askt in total loss is set to 0.1. After freezing the network weights, we sesueentiall sequentially fine-tune
The
individual components for one epoch each: optimizing weight clipping ranges in the first epoch, activation clipping ranges in the second epoch, and bit adaptation module parameters in the third epoch. Each stage uses the Adam optimizer [24] with batch size of 2. The initial learning rates set to 1 x 10-3 33 for weight clipping ranges, 1 x 10 for activation clipping ranges, and 0.1 for both temporal shared layer bit adaptation and flw-gradient video bit adaptation modules. Each learning rate is decayed by 0.9 every epoch. All experiments follow the same configuration described ahove, and all parameters are fixed across datasets and models ensure fair and consistent evaluation.
4.2 Comparisons with Efficient Video Super-Resolution
We measure reconstruction accuracy using the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) for evaluation metrics. As shown in Table 1, our method achieves superior performance and establishes new state-of-the-art among efficient VSR approaches. Compared to KSNet [21], our model requires fewer parameters yet outperforms it by 0.12 dB in PSNR on the REDS4 dataset. Compared to the SSL [42] method, our approach maintains comparable model complexity while eliminating the need for ground-truth (GT) videos during training. Notably, we achieve performance gains of 0.2 dB REDS4 and 0.31 dB on Vimeo90K-T (BD degradation). Moreover, we retrain a unidirectional variant of BaR (BasicVSR-uni) by removing its back- ward propagation branch, yielding distinct yet functionally consistent architecture to assess the generalizability of our quantization framework. These quantitative improvements across multiple benchmarks convincingly demonstrate the effectiveness of our quantization framework.
Table I: Quantitative comparison (PSNR SSIM). All results are calculated on the Y-channel except REDS4 (RGB-channe) Blank entries correspond results that cannot be reported. The results for PQ and FQ correspond to Partial Quantized and Fully Quantized models, respectively.
Methods
Params(M)
GT
W/A
REDSA [33]
degradstionn Vimeo 90K T [4:]
[104
UDMIO (45)
Vimeo 9UK [4:)
degradation
Vidi (30)
Ricabic
14/0.220
31.53/0.8684
23.76/0.6947
78:47 /0825)
0.550/
21 8
VESPCN
25.35/0.7557
SPMC [96]
25,85/077552
(44)
27.96
33.08 0.9004
25,89/0.0651
36.26 00.438
34.62/0.921
DUF
28.63 70.825
28.48 /0.9605
0.839
RDPN [13]
30:09 70,8500
37.07
27.1270.8188
28.56/ /0.559
37,20
BDVR
30.53/ /0.8699
37.09
20.10 0.8116
01.663
37,53
0.8406
PFNL [45]
32/32
29 /0.850
36.14 119963
26.73/0.8099
38.74 /09627
27.16 00.355
TGA [18]
32/32
37.59 0.9516
(0.4423
RI [10]
38:48 9606
36.40/ 04.4
27.48 0.8388
RSDN [17]
39 9653
37.23 /0.9471
27 23.92/ (08500
RRN (19)
32./32 92
38.96 0.9644
23. 0.8488
DueDDDeet (43)
2.5
32
36.12/09348
26.14 14.8099
RiscVVR (3)
32.322
80,900
33.18/0.499
23 2
29.06/ 7099994
$7.5 (1.499
27 108553
Iosc (42)
32
30.56 708733
36.57 (18899
38.98
00.9645
36.78 (1.4511
27.27 008327
Lymmem [25]
32.332
30 (6 8786
36.09 1084
39/04/ 9650
36.89 64 0.9437
27 008335
ASSI. [46]
30 808700
23.01 10.50 76
39
9690
03 04540
27 0.8400
KSNet [21]
1.5
/2/332
31.14/0.800
27/22/0.520
27.54 00.5003
SSL [42)
1,0
32. 32
31.06/ 708883
36,82 708119
23.15/ 70.520
29. 35 / 9665
37.06 0..455
27 75 0,84431
OursPP
1.3
6/ 6MMP
31.26 / (L8879
37,07/ (1/0400
27.18/ / 10.0215
39.64/ / IL9600
37.37 (1.9855
27.84/ / 10.495
OursiP
1,0
6/ 601P
31.17/ / (188499
36.79 (1/849
27.05/
0.83340
39 22/22/ / 1L9646
37,12/ 11.944
27.69 10.4055
Ours:P
1.0
47 4MP
30.34/ 018657
15.93/1.3315
26.26/
0.7764
38:15 / (L9576
36.46 11.9387
27.02/ 008161
Ours+FQ
0.7
4/4MP
30.26 / 0L8637
35.82 /0.9111
26.29
/ 0.7752
37.59/ / (L9536
35.05
11.9399
26.81 0.9925
Busic VSR ani [3]
2.6
32 32
30.54/20 00.8094
(U.929
23.03 708563
29.29/ / 019646
37,27 (1.9473
27.54 70.4499
BSscVSX uni life (42)
0.7
32
29:95 /0.8511
36.38 (09372
26.65 0,9002
38.24/ (19596
36.38 10.9088
26.87 0,8157
Ly-nam-ni [25]
0.7
12.130 32
29:97 /0.8570
36.A5 (1.9381
26. 70 00.001
36.53 /1.94455
26.89 00.3377
ASSL-uni [46]
0.7
32
30.02 /088500
36.49 (1.9355
26.76 00.001
38.48 01.600
36.61 /119416
23.02 / 03.236
KSNet-oni [21]
1.6
32./32
30.69 /08724
27. 14/ 00.600
37. 34 /.55490
SS1 -ani (42)
0.5
33./ 92
30.24 808663
36 56.66/ 36.56/0.9992
23.01/ 00.81
38.68 /0.0615
36.77 / (..420
27. I8 F.B//O.KE 0.8306
Ours-mi-PQ
0.8
6/ 6MP
30.40/ / 0L8665
36,81 /0.9413
26.91 (0.800
38.97 / BL9633
37.11 (1.466
27.40/ / 008372
Ours-uni-FOQ
0.55
X
6/ 6MP
30.35 / 0.8666
36.63/ / 00.9396
26.81/ / 0,8060
38.770 / 0.9610
36,83/ /0.9434
27.29 / 0,8310
Ours-uni-PO
0.7
X
4/4MP
29.57
/ (.8460
35.96/ 00.333
26.32/ / 0.7784
37.77
/ 0.9550
36, 23 /0.9366
26.71 / 0.9067
Ours-uni-FOO
0.4
X
4/4MP
24.55 / 0.34334
/.9926
26.15 / 0.7674
37.29 / 0.9517
35.72 /1.9319
26.53 / 0.7938
4.3 Comparison with Static Quantization
To substantiate the effectiveness of our method, we benchmark against conventional static quantiza- tion techniques without QAT. Comparisons include standard PTQ approaches MinMax [20] and Per- centile [27]. To ensure fair comparison, we extend these methods by incorporating fine-tuning (FT) steps to existing PTQ approaches, denoted as MinMax+FT and Pereentile+FT, where FT employs
8

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our parameter optimization strategy described in Sec. 3.5. In addition, we also adapt DBDC [38], the first image super-resolution quantization method, into the video setting. Since video SR networks typically adopt recurrent architecture with parameter sharing across frames, image-specific quan- tization strategies cannot be directly applied. To address this, we apply DBDC"s clipping-houndan estimation independently on each frame, and then aggregate the per-frame boundaries via an EMA strategy across the temporal dimension. This modification preserves the core idea of DBDC (dy- namic clipping with EMA) while making it compatible with recurrent video SR models, thus pro- viding a meaningful baseline for comparison. As Table 2 demonstrates, our adaptive quantization surpasses all counterparts in 4-bit weights. Extended experiments validating the applicability of our method are included in the supplementary materials. Qualitative comparisons are shown in Fig. 5, where it can be seen that only our method successfully restores the contours.
Table 2: Comparisons with static quantization on VSR. All results are calculated on the Y-channel except REDS4
"Time (min)" indicates the total processing time for each method.
Methocs
FT
W/A
Time [min]
REDS4 (53)
Vimep-0UK--T [-4]
BI degradation
Vi64 [30)
(DM10
BD degradation Vimee (44)
Vid- [30]
RssieVSR [3]
32/32
$1.42/ 0.809
37.16/0.9450
27.24/0.8251
39.06/ 0.0969
37.53/0.9408
27 96 008553
RavieVSR-MiiMa [20)
X
4/4
28. 12/12/000 7896
34.90/0.1183
26.01/0.000 /0.755
35.37 / /0.9251
34.52/0..106
25 64 0.122
BasicVSR Percentile (27)
x
4/4
3
0.7861
24.37 /0,919
25.23/0.7300
35.06 0.9041
34.11/0.913
25.08/ 1..00/.775 0.7275
Hosie DSR-DBDCC [3S]
4.74
30
28.07/ (.7817
35.23 00,922
26.34/0.7720
35.06/ (15339
54.77/0:9122
26.01/ 0.7558
BavicVSR- VSR-MInMaxx-FT
4//4
70
29.21 /08238
35.22//..922 9212
26.17/0.7601
36.44/0 03.4//0.9390
34.88/6.9176
26. /0 10544
Basic VSR PercmallesFT
70
28.30/ 0.8054
24.40 0.9159
25.26/0.7314
35.32 /0.3322
34.20/0.9115
26 0.7340
VSR DBDC-FT
4//4
95
29.24/
35.45 /0.924
26.31/0.7705
36.82 /0.9444
35.25 00:9228
26.42/ 0.7764
MaseeVRR-Ours
4/4MP
30,26 0.8637
35.82/0.9311
/0.7752
37.59 (.9536
/..9339
26.81 0.8025
MinMax 24.28 O 066666
Pencentile
DBDC 24.53/ 0.6864
MinMax-FT FT
24.13 0.6642
24.69/ 0.6883
Frame 619, Clip city, Vid4
Percentle-FF 24.23/0.6699
DRIC-FT 25.08 0.7223
Ours 25.73/ 0.7770
PSNR SSIM
Figure 2:
Qualitative comparison on Vid4 [30]. Only our method reconstructs both structural con- tours and fine-grained details. (Zoom-in for best view)
4.4 Ablation Study
Table reports a component-wise ablation on REDS4 with 4-bit fully quantized Bas.c The first row corresponds to the full-precision (FP32) baseline, where calibration is performed and the network therefore runs without quantization. Activating calibration alone (row 2) applies 4-bit quantization and causes the expected accuracy drop. Fine-tuning the small calibration set (row 3) restores most of the accuracy lost after calibration. Enabling FG-VBA or TS-LBA individually (rows 4-5) provides further gains, and combining both adaptation modules (row 6) delivers the best overall performance, surpassing the FP32 model in perceptual quality, while being constrained to 4-bit quantization. These results demonstrate that each component, calibration, fine-tuning, and the two adaptation modules, contributes significantly to the final reconstruction accuracy.
Table 3: Ablation study on each attribute of our method evaluated REDS4 with 4-bit FQ Ba- SSR FG-VBA and TS-LBA denote the flow- gradient video bit adaptation and temporal shared layer bit adaptation modules. Calib is the calibra- tion, and FT denotes fine-tuning. The full configu- ration gives the best reconstruction accuracy.
Table 4: Comparison with SOTA efficient VSR methods. Pretraining denotes whether a pretrained model has been used, Data means the number of video clips required for train- ing, GT denotes the requirement for ground- truth HR videos, BS is the batch size during the fine-tuning phase, and GPUs denotes the number of GPUs used. The processing time is measured A6000 GPUs. The runtime is computed based on an LR size of 180 x 320.
FG-VBA TS-LBA
Calib
FT
PSNR
SSIM
31.42
0.8909
28.34
0.7933
/
29.05
0.8180
Melaah
Putnasing
Dat Or
Jaroices
Pascrecing
Rarins: GPCs
/
29.96
0,8502
BaikSk
36690
300/00
116k
S3
V
30.14
0,8606
30.26
0.8637
SSL 143
36000 7
363.30
3708
Stmis

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4.5 Computational Efficiency Comparison
We analyze the processing time of our framework in comparison to existing SOTA efficient meth- ods in Table 4. In terms of processing time, our method is over x x200 faster than SSL [42]. The calculation as follows: 370 hours x 60 = 22,200 minutes; 22,200 minutes 90 minutes R 247x. Furthermore, our method only requires 1 GPU, whereas SSL [42] requires 8 GPUs. These results demonstrate that our method requires significantly less processing time and fewer GPU resources compared to other SOTA efficient VSR compression methods. Note that this comparison specifically evaluates model compression techniques applied to pretrained VSR models, rather than comparing quantization with core VSR training. Both SSL and our method are compression approaches oper- ating on the same pretrained VSR backbone. Beyond the drastic reduction in tuning cost and GPU resource demand, other methods, making the quantized model highly efficient for practical deployment. Combined with its minimal data requirement (only 100 video clips without the need for GT), our approach presents a highly practical and efficient compression solution for VSR models. To the best of our knowledge, this work presents the first successful quantization scheme for video super-resolution models, delivering a breakthrough in efficiency while maintaining competitive performance.
method also achieves superior runtime of 8 ms. This is notably faster than
4.6
Error Bars Evaluation
PTQ for VSR requires a calibration dataset of unlabeled LR video clips to estimate activation statis- tics. An important consideration for practical deployment is the sensitivity of the quantized model's s performance to the composition of this dataset. Notably, these calibration data can be sourced di- rectly from the target application's 's video streams, eliminating the need for separate data collection. In our main experiments, calibration data were sampled using a fixed random seed for consistency. To rigorously evaluate the robustness of our method, we constructed multiple calibration datasets by sampling 100 LR video clips using different random seeds (0, 1, 2, 3, and 321), where seed served as the default in our main experiments. The performance variations across these datasets are quantified in Table 5. The results show consistently high performance with low variance across dif- ferent calibration sets, confirming that our method is robust to variations in the calibration data. This demonstrates that in practice, any representative sample from the deployment domain can be effee- tively used without requiring carefully curated data. Our approach therefore provides both superior quantitative performance and practical robustness for real-world applications.
Table 5: Error bars evaluation with 4-bit fully quantized BasicVSR (PSNR SSIM). All results are calculated on the Y-channel except REDS4 (RGBB-channel. The random seeds are taken from 0, 1, 2, 3, 321 to generate calibration datasets.
Methods
seed
REDS4 [33]
Vimeo-90K-11 [44]
Bl degradation
Vid4 [30]
UDMI0 [45]
BD degradation VineosOKK- [4-]]
Vid4 [30]
BasicVSR [3]
31.42/ / 0.8909
37.18/ / 0.9450
27.24 0.8251
39.96 / 0.9694
37,53 / /0.9498
27.96 0.8553
30.09/ / 0.8562
36.03/ / 0.9335
26.29/0.7772
37.78/ / 0..554
35.93 /0.9336
26.80 0.8000
30.26/ / 0.8637
35.82/ / 0.9311
26.29/ / 0.7752
37.59/ / 0.9536
35.95 0.93399
26.81 / 0,8025
29.90/ / 0.8511
35.81 / 0.9311
26.22 / 0.7738
37.94/ / 0.9563
36.02/ / 00,9347
26.77 0.8032
Ours
30. 19 0.8618
35.93 / 0.9320
26.19 0.77
37.85/ / 0.9557
36.08 0.9355
26.93 0.8 14
321
30.09/ / 0.8598
35.77 / 0.9299
25.84 / 0.7530
37.78 / 0.9546
35.97 / 00.3355
26.88 0.805
Mcan
30.11 / 0.8578
35.87/ / 0.9315
26.17 / 0,7701
37.79 / 0.9567
35.99 009334
84 / 0.8053
Std
0.12 / 0.0038
0.10 0.0011
0.17 0.0088
0.12 0.0027
0.05 / /0.0199
0.06 / 0.0032
5 Conclusion
In this paper. we present the first quantization method for VSR with a few unlabeled calibration video clips that adaptively learns the bit adaptation strategies. Different from image SR, the temporal error propagation, shared temporal parameterization, and temporal metric mismatch significantly degrade the performance of the quantized VSR model. To alleviate these problems, we propose temporal awareness adaptation post-training quantization framework for VSR. With the flow-gradient video bit adaptation and temporal shared layer bit adaptation, we could get the initial bit-width for videos and layers VSR models, then optimize them with calibration and a novel fine-tuning method with the supervision of the full-precision model. The results demonstrate that our method not only achieves superior accuracy compared to SOTA methods but also significantly reduces the tuning cost and achieves an ultrafast runtime, underscoring its high practicality.
10

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Acknowledgement. This work was supported by the National Natural Science Foundation of China (Nos. 62276182, 62476196), Emerging Frontiers Cultivation Program of Tianjin University Interdis- ciplinary Center, Tianjin Natural Science Foundation (Nos. 24JCYB/C01230, 24JCYBJC01460), and Peng Cheng Lab Program (No. PCI.2023A08).
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A More Details on Datasets and Evaluation Metrics
Training and testing datasets. For video super-resolution at a scale factor of 4, we train the model using two distinct datasets. In one setting, we generate LR frames by applying the MATLAB imresize function with bicubic interpolation, and the model is trained on the REDS dataset [33], using the REDS4 subset, which consists of clips 000, 011, 015, and 020, for evaluation.
another setting, the model is trained on the Vimeo-90K dataset [44] using two types of degradation: bicu- bie interpolation and a olur-downsampling approach that applics a Gaussian blur with a standard deviation of 1.6 followed by subsampling. The evaluation is conducted on three datasets, where Vimeo-90K- T [44] and Vid4 [30] are tested under both degradation settings, while UDMI0 [45] is evaluated using only the dowsamlonnngl setting.
REDS [33] is a standard proposed high- video dataset with resolution of 1280 x 720, designed for video super-resolution. It includes 270 clips that serve both training and validation purposes. Following the setting [33], we use REDS4, which consists of four representative clips (000, 011, 015 and 020), as the evaluation set, while the remaining 266 clips are used for training. This dataset is employed for training video super-resolution models under bicubic degradation.
Vimeo-90K [44] is a widely used video dataset with resolution of 448 x 256, commonly adopted for video super-resolution tasks. provides 64,612 clips for training and 7,824 clips for testing, with the test set referred to as Vimeo-90K-T, This dataset is utilized for training video super-resolution models under both bicubic interpolation and dounamonssn degradations.
Vid4 [30] is a classic dataset commonly used for evaluating video restoration methods. It comprises four video clips named calendar, city, foliage, and walk, each consisting of at least 34 frames with resolution of 720 x 480.
UDM10 [45] is standard benchmark dataset introduced for evaluating video super-resolution meth- ods. It consists of 4 video clips covering diverse scenes, with each clip containing 32 frames at a resolution of 1272 X 720.
Evaluation metries. Following [3, 33], the evaluation metrics are computed on the RGB channel for REDS4 [33], while for Vimeo-90K-T [44], Vid4 [30], and UDMI0 [45], the calculations are performed on the Y channel.
B Additional Experiments
B.1 Per-stage Fine-tuning Analysis
As detailed in Section 3.5, our fine-tuning process consists of three sequential stages. This design ensures stable optimization by progressively refining different subsets of quantization parameters. To provide a comprehensive analysis, Table 6 reports the performance and feature average bit- width (FAB), which is calculated across all video clips in the test dataset after each stage, with the analysis conducted on the VSs architecture under an FQ setting.
Stage 1: Weight Clipping Optimization. In this initial stage, we exclusively optimize the clipping parameters ((u K for weights, while freezing the quantization parameters (17 ) k==)) for activations and the bit adaptation parameters (fb%, } 1-26 2,22). The fact that this stage alone already surpasses strong baselines like DBDC [38] in accuracy, while maintaining a lower FAB, demonstrates key finding: our framework, even with its bit adaptation modules not yet finely tuned, can achieve a superior serformance-efficiinn trade-off purely through optimized static quantization of weights. This establishes a strong and efficient bascline.
Stage 2: Activation Clipping Optimization. We then refine the clipping ranges (L1 K for activations, with other parameters fixed. This step addresses the dynamic, input-dependent nature of activation distributions. We observe a consistent performance improvement across all datasets with a negligible change in FAB, indicating that the activation quantization parameters are being effectively tuned without compromising efficiency.
Stage 3: Bit Adaptation Modules Optimization. The final stage unlocks the full potential of our framework by optimizing the bit adaptation parameters, including the TS-LBA factors ((t)))) K and the FG- FG- thresholds (lo26, 4.26). As shown in Table 6, this stage yields significant per-
15

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formance boost by enabling dynamic bit-width allocation, at the cost of a moderate and controlled increase in FAB. This introduces tunable trade-off: users can opt for the high-efficiency model from Stage 1 or 2, or the high-pefformacc model from Stage 3, depending on their specific deploy- ment constraints. Note that the weight bit-width remains fixed at the base precision (e.g.. bits in "w4a4") throughout all stages; only the activation bit-width is dynamically adapted.
Table 6: Performance and efficiency analysis across different fine-tuning stages. Results are reported in metries of PSNR / SSIM / FAB. All the results are calculated on the Y-channel except
REDS4
Mathods
WiA
Time (m1n)
REDSA (33)
degradation Vineo 90K T [44]
V164 [30)
UDMIO [45]
VimeefOX T [44]
BD degralation
Vid4 [20]
DBDC [38]
0.7817
0.9255
70.720/
(09339
34.77 70.9172
26:01
Ours Stuged
29.82/ 0.8466
35.46/ 0.9366
1612/07666/
(0.9444/ 3.70
35.45 /8.222
3.75
Ours Stage3
29.8
0.9270
0.9471
3.755
Ours-Stage3
0,83775.50
/03901/553
36.15
5.08
26.81
B.2 Full Quantization vs. Partial Quantization
In this work, we compare VSR networks with existing static quantization methods that do not uti- lize QAT. Meanwhile, the majority of SR quantization approaches employ a partial quantization scheme, restricting quantization exclusively to the network's 's body module. Therefore, we analyze the effect of fully quantizing the network in Table Despite its limited impact on reducing total computational cost, partial quantization yields notably higher reconstruction accuracy. Overall, our method delivers higher accuracy while maintaining comparable computational cost in both partial and full quantization settings. For the evaluation metric, we measure reconstruction accuracy using PSNR, SSIM, and FAB. We further validate our quantization framework on the unidirectional vari- ant, BasicV of the table, our method consistently outperforms existing quantization schemes on both BSR and BasicV -S-- confirming the robustness of the proposed design.
to assess its generalizability across architectures. As shown in the lower part
B.3 Comprehensive Error Bars Evaluation with FAB
In our main experiments, calibration data were sampled using a fixed random seed (Seed 1) for con- sistency. To provide a more comprehensive robustness analysis, we extend the error bars evaluation presented in Table 5 of the main text by further including the variation in FAB. As in the main text, we use random seeds (0, 1. 2, 3, 321) sample calibration datasets. The results, presented in Table 8, demonstrate that our method exhibits not only stable performance (PSNR/ / SSIM) but also a sta- ble FAB across different calibration video clips. The low standard deviation in FAB confirms that the bit-width allocation is robust and does not fluctuate excessively with the calibration data.
B.4 Comprehensive Quantitative Comparisons with Perceptual Quality Metrics
As GT is not available for real-world videos, full-reference metrics such as PSNR and SSIM can- not be applied in such cases. To provide more comprchensive assessment, we additionally report two widely used no-reference perceptual quality metrics, NIQE [32] and BRISQUE [31], on the REDS4 [33] and Vid4 [30] datasets. The corresponding results are provided in the Table 9. Com- pared to the efficient VSR method SSL [42], our 6-bit models 1.00-.3M) are competitive or better in PSNR SSIM while using far lower bits. Compared the FP BSR (32 / 32, 4.9M), our 6-bit models narrow the fidelity gap to within 0.2-0.3 dB on average while using 4-5x fewer param- eters, and with perceptual scores that are comparable or better in several cases. In addition, under the same 4-bit FQ setting, our method consistently delivers higher fidelity and strong perceptual quality than standard PTQ basclines, including DBDC [38]. On REDS4 [33], our method (4 4, FQ) improves PSNR over DBDC+FT by +1.04 dB (30.26 vs. 29.22) with a lower NIQE (20.71 vs. 20.98). On Vid4 [30] (BI), we achieve comparable PSNR (26.28 vs. 26.31) but with better NIQE and BRISQUE (21.20/ / 45.30 vs. 21.91 / 46.83). On Vid4 [30] (BD), our method outperforms DBDC+FT by +0.39 dB in PSNR (26.81 vs. 26.42) and achieves a lower NIQE (21.27 vs. 22.01). Furthermore, to validate the generalization of our method across different architectures, we also evaluate on BSsieVSR-uni variant. On this architecture, our quantized models maintain competi- tive performance against the full-precision baseline while utilizing significantly fewer parameters and consistently outperform other PTQ methods, demonstrating robust generalization capability. In summary, unlike other PTQ methods (MinMax [20], Percentile [27], DBDC [38]) that often trade
16

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Table 7: Comparisons between fully quantization and partial quantization VSR in terms of PSNR, SSIM and FAB. All the results are calculated on the Y-channel except REDS4 (RGB- channel) and FQ denotes Full Quantization models.
degredattonn Mines-50K (44)
degiatatoo
REDS4
Vida
UDMIO (45)
VareelOK-T
[SH[
BascVSR (B)
32132
31.42 0089001 $2.00
57.18/ (3.050033.00
27.2470.2251
52. 00
30556/ ELO6AA 32,00
(0.53/ 33.00
7736/888553--4
52.10
(4000
HuizYSK Posemnile [27)
4.24
225 29
(.8201 44000
26,88 669345 5.00
26.31/ /0.9099 (4003
31.50 F03051 4.50
35396/ (X5348 4.00
26.82
(0:2-1030 50000 4400
BaseVSR DEDIC (SS)
41.4
28-11 7835 1400
35.297 (UR232 6.00
26.27
(4.0)
36314 (030300 4.00
34.84/ RU1127 4.00
2030 7289 4J90
Bascy Mi-Miiiii
21
4400
35.25
26
36-42
7520/4400
PercentiosFT
11/315 (..0
7968
(0.9513 4.60
56.16 /ISSSS 000
5075 (.00)
45..
(133133 1.00
35 AS 110000055// F1L00
76 12: /0 103066 7-300
YUME N0 8.00
32. INS2A1 L1
26-11 7755/1192
BedeVVSR-Ourrs
474MP
20.34 /0.96577 5.48
35:832 8/9138/ 3.95
26.36/110 /6.77661 3.75
/8.15//9956//6 6.75
16.66/ EURN77 FU0S
27.10 (1.8611/111//// /4396
Bus MinMax 120)
4.74
08112/0.00 00.709661111 1400
34,89
(4.00
7412/400
RaiiVSR Peooneile (27)
4144
27 73 (22/067744 74.00
31.37/ 0.9159 /4.00
25 25 /0. TATT3 (4.00
15 (0.3341 /400
34.11 RDS135 (5.04.00
25.00 (D 7275 /4.00
BaseVSR DRDC (391
42.4
75.07 (0. 7017 74.00
35.23 009225 1-00
26
77200
14.00
35.9G /0:3390 4.00
54.TV/ IXS12 4.00
20.01 7541 74.00
BasEVSR
PercentiesFT
9054
26 7314 (4000
35 70.3362 /4.0
54.20/ (154//5112-40
25.26 7540 4.00
Bese DRDCHFT
47.4
(03333 4.00
35. 05:0555 1-00
26. 31 /0. TTE3 (4.00
30-32 (0:04347 4.00
35.25 F05228 4.10
24-02
7764 4.000
BosieYSR Ours
4/4MP
34.26 0126670.677550 5.50
35.82//9911/ /585
26.29 (UL7753
14/0
37.09 07..9//0955///60 4.78
S5.8// BU935 5.00
26.11 /1.3025 4495
Booee Minbtte (20)
6.25
31 19/00.0955/11 5.00
27 11/00 10.03
74 151/0.9000 000.00000777 7800
31.25 /DS510066000
5156 75.00
BisEYSK Ponomile [27]
6..05
31 D5 (03201034511 76.00
16777 000930 6610
26 0e /08E21
(0.00
11 70 0654654/7 9.00
A RESAR 6.10
1170 2.000
Resevse-DDDC:11 189)
31 12.120088111 5.00
74A1 70-877/ 8.00
31.29/ 18//888660000 11
23107/08551/7 76.00
/0.9655 5.00
36.01 115165-6.00
(08500 75.00
6.66
31 70.0008891100 78.00
24 20.0000000/00000 8.00
(138072) 7.000
BeleVSS-OOrr
6/6MP
31.26 0.8879/7.44
3TAT7/6944//77
7.87
/6.73
39.44 /9.4/99999/677
ST.37/ (U945 7,06
27.54 /0.5495 /6.97
Bovi Mirdttc (10)
6,75
31 /0/000011
16: 1679208411 F6/0
90 211400637/600
(ISSITS 76.00
BaicYSR Pascatile [27)
6/6
25 22 10.2095 56000
34,707 002211 16.0
42 1L.451 (6:00
00.9400 5:00
34.40/ R89106660
25 7485 5.00
BaieYSR
62.5
31,07 /0.22222111 6:00
35.852 02141
13//0/99 (6.00
31 (00651
1894537660
22-72
5.00
Mis-aii PV
Buak PerestteeFF FT
6266
29 1212/ 0083//3333//0 6.00
34.75 709210 6,00
51 /L.749 (6.00
35.35 000435 5:00
54.55 105218/61 6.00
50 7571 75,00
Beag' 58 DBC-C IPT
6/66
31 00225225111 1600
35427 00442/
11000/111111111 15:00
31.25 /000647 5:00
ST.06/
1S544816 6.00
72
(117 5.000
RoicYSL-Ours
6/1MM
3L177/0286/774 7.47
36.79/6.5499
37.05
39.22
6,66
3T.00/ 0.944476955
17.0 /0.545// 5.36
HserVSHt-mi [S]
122
30.54/ 0U859 (2
36.90 (L9629 22200
21 123 70.81577 32.00
39/29/
ELUBAO 32.000
57 27 (0.922 $3.00
21.34/
01.44/05499/20 52.10
Buac' 58. asi
03100
(0.1002
(55153440 4.00
Pavarile
7963 74000
BaskVSR un DBDC [30]
414
29 0.77664/ 4.00
80 (0, 1A75044 (4.00
33.20 0000337 4.00
54.75 055500 4.00
7622 /4.00
BaacVSR us MicMac IFT
41.4
25 25 (03157 1400
25 8O 7-000
(0.0997 4.00
34.92/ 089188 4.00
20 1O 7591 74000
BeskV5R..nn PT
1000
1000
DS5100
761. 7.400
Bask Oues
4/4MP
24.57
00.57/03460//5 /533
353676.9335/ 5.12
36.38 (L.7766 7.775
37.78 0.9550/ 5.00
36,23/ 0.3367 5.0
26.71 /0.9577777 74.99
Buaic Marttss (256
S6. /4.00
35.53
/0.0244445
7521
74000
BascVSR on DBDC (2])
414
(0.2033 7400
34.1 70018691 4.00
86 (L.310 (400
35.65 (000277 4.50
34.34/ (091722422
25.06 790S /4.00
42-4
90
74000
10201009100
T991 1.00
4.00
54
1a 7W07 24000
4:4
79/0 (400
/0-5577 4.00
1E.0//D /00/0//00001/1 (.00
(D 761 7440
4/PMP
29.85 03.5/435444552
0575/6/9296/1
2.12
3615/107664/4 4475
37.29 /7.99/ 5:87
35.71/ IL9519 (509
/0.70 /499
MinMes
26
BaieVSR Pasonill
00.56/00000 60000
36.51
(051/00545511 76.0
8252 76000
Book oni DBDC [30]
6/16
30 40 (0 9051 76.00
367570099111
7600
2A 10.0/10 /0.1/775770 2-00
SEMI 20.0600/ 6:00
36,90 (1546446.00 6.00
27 ST (0 8 5.00
Baacvss MirM-------- MiTMax-FFT
67-
30 (0 00533 76.00
16774-0980444 7610
24 10.9//50001 5091 20:00
29.55
/0850/000//000 6.00
16.5/ 189456 10
22 (0 1.000
BuacVall DT
62.6
39 (.29912 5.00
200446090014 16.00
56.70 (0.9611111100 Kd00
(35440-6.00
22 (0.52) 75,00
Bask mni-
6/6MP
30,40 0000//0965//0 (6.23
36.51 (6341/6.13//55 7.45
26.91 00L8000800/000 6660
35,97 /8/633//7 7307
35.13/ (L9660 7.00
27.40 27.40/088372 6.97
HaicVSk ane desomile [27]
6255
25 (5/72/08221/11 15.00
26.12 6:00
25.0//L12222 (L.1222 -6.0
35. 70300333333 NJ00
34.32/ DISINT 6.00
25 16 (0 (227 78:00
BeseVSk one DIIC (20)
6266
-33 33 (0.0633 70.8611/6000 76.00
26 NO. TWL//8022/00 (690
04/00/00004// N:00
16.117 189426 6.00
27:51 (0X315/ 2.000
6/55
(.10)
1190/13555 (610)
39.8 /03009 N:00
36.33 18041556610
2227 75.00
Banchall oms Di0i
62.6
-32 00.00/0.002//11 16:00
39 (6.1/1000--63
/0.0899/ NJ0
(85416 6.00
22.25 (0 75.00
Basic BouccSS--uii--ae uni--rrr
6/MMP
38.26 /0.8646/7.25
36A37 6.3366/ 7.42
26.81 /1L0000002200 6675
38.70/ 95.70/896000/33 6:91
36.55/ (L9450 6.94
27.39 0U8309/6.82
Table 8: Error bars evaluation with 4-tit fully quantized Basic (PSNR / SSIM/ / FAB). All results are calculated on the Y-channel except REDS4 (RGBBchannel). The random seeds are taken from 0, 1, 2, 321 to generate calibration datasets.
Medbods
seed
REDS4 (33]
Vimco-90le T [44]
BI degradation
Vid4 [30]
UDMIO (45)
BD degnddation VincooiiiCT [44]
Vi34 [30]
BasicVSR (3)
31,42/ O. 0,8909/ 32,00
37,18 /0.94500 32.00
27.24/0.825| /2.000
39.96/ 10.66709694/// / 32,00
37.53 (19498/ / 32.00
21 97.5 08557 / 32,00
30099
36,03/
26.29 /1.77////////3.
10.5544
(09935
30.26
/07775/
/0.95% /4.78
0.93399
29
35.81
1
26.22
/0.9633
Oars
/
00,8508/
31.11 /0.8558/
35.87 0,9315
26.17 /0.7701 /.77
119551 /4.92
35:99/ 053-99/0.9333///
26.84 / (0053
0.12/ 0.003/ /0.24
2000//001///000
0.17 1117/00088////0
0.12/00027/0.11
0.05 /0001//0000
0.06/ /00032
off distortion and perception, ours achieves reconstructions that are both quantitatively accurate and perceptually natural.
B.5 On-device Latency and Memory Consumption
We present CPU evaluation across three benchmark datasets, REDS4 [33]. UDM10 [45], and Vid4 [30] running CPU-only inference on both Intel Xeon Gold 5218R (x86; Linux; 40 threads used) and Apple MI Pro (ARM; macOS; threads used). Our quantized models use 6-bit weights (64 levels) and dynamic 6-bit activation precision, executed via INT8 kernels due to current deploy- ment constraints. Our method achieves consistent speedups across both platforms. On Intel, we ob- serve 1128-1.45x speedups over VSS [3] and 1.05-1.14x over SSL [42] across all datasets.
17

---

Table 9: Quantitative comparison (PSNR*/ /SIM / /NIEE// BRISQUEL). PSNR and SSIM are calculated on Y-channel except REDS4 [33] (RGB-channel). NIQE is computed on grayscale, and BRISQUE on RGB, following the PIQ [23] implementation.
Mediods
Pg ParamstM)
GT
WFA
REDSA (551 (BI)
Vide (30)
Vide (B))
BSsieVSR [3]
4.9
32/32
3142/08889
4534 42 0503
27.24/ 72244/0825/ / 2263/ 46 68.20
27.96
9958 /44.2148
SSL, [42]
1.0
32/32
31.66 /0833/
5775 51.5137
22.15
24494 (4644662
27.56/
8431
9479479/// 44.2 2.00
Ours
1.3
6/6MP
31.66 1126/0888
0.877/ 20.5416/ / 42.6729
27.18/ 0.215/ / 22111655 -46.5961
27.8/ 0.8495 21.2233//////// 41,332
Ours
1.0
6/6MP
31.17/ /
41.9915
27.05/ 0.510//11.177)/
445.7253
27.69 0.8405 21.0.990/ 43,9466
Ours
E.
4/4MP
/666// 30.9005 46.994%
0.776.7////2 21.10002 /47.0901
27.02/ 0.8161 21,5588 44.8271
Ours
0.7
4/4MP
6867)
20.70069 46.0018
36.29 (12752 21..1999 /45.3042
0.8025
21,3659 44.4966
MinMax (20)
0.2
4.4
28.12 7556 20-56.0/ 6086
26.00 /0.7555 /1.7752//48
25.68: 74112/2 21165558
Percentle (27]
0.7
4.4
27.78 0 20.5555
8630
25 23 0.7905 21 00.900791 0859
0/7275 25.00
38566666
DBDC [38]
0.7
424
28.7 0.7817 222252255 59.5703
26. 2.220/7722//22
26 0.7551
6523
1941
MinMaceFT T
0.7
414
29.21/
/ 0.8288 21 1090
/45.460080 -5.4638
26.17 0.7631 21.9205 45577766
26.11 0.775A
22.28044 1-304/43300000 /43.5093
Percentlle IT (27)
0.7
414
20.30 0.806 20.3430/
1643
25,26 0.7314/ 21.3187 48. 1876
25.25
0.7240 21. 11.5298/ /-55 3054
DDDC-FT
0.7
474
29.24/ 0.8232/ 20.9791/ / /44.9058
26.31 0.7900/ / 21.9100 16,8330
25.12 0,7768 22.0107/ /3399439
RRiicVSR-uni [3]
2.6
32/32
30.54 80894
27.03
(056
D.8419; 9
/43,
Ours
0.8
6/6MP
30,00 9.865
20.6781/4 /41.1266
26.91
2396 46.7119
27.30/ 0.8372//
20.853 /41.3151
Ours
0.55
6/6MP
30.35 8646/ 20.7033/4 43,1997
26.81 0.500/
0356 28628837
27.29 D.8310/ 20.5523 /41.5799
Ours
0.7
4/PMP
29.57/ 8.800/
20,5091 /5500227
26.72/ 0.7764 21,7651
44:2250
26.71 9.80677
2121.1994/ 44.7664
Ours
0.4
4MP
29.55/ 8.844/ 20.7997 /4.77569
26.15/ 0.7674 7846/ 446.7455
26.53 0,793N 21.2562/ 481.8501
MinMax (20)
0.4
414
28.45 00 /
21.1811/ 15,3900
25.56/ 0.7308
21
/43.3372
25.82 0,7531 /221 /43,3335
Percenilee (27)
0.4
414
28.12 0.798
7988 20.8874 47.1888
24.81 0.7000 21 1476699
24.8710.71 1.77133 /22.24114
06.8296
DBDC [38]
0.4
424
28. 077553 11.18 69.8
25. 86 0.7510/ 21. 1411111155
1-415-555 2426
25.95/ 00.7608: 21.1995 /43.5195
MinMaxFFT (20)
0.4
414
2836
O.EES 21 2081
44.9757
25.72 0.7354
2928
-45-2211
26.14/ 1277677
21.77356 /45.11558
PerceatlesF1 [27]
0.4
414
28.35
43.3349
24.92
0.7105 21.5508 488.9833
25,09 21.7242//21.3033 /46.2203
DBDC-FT (38)
0.4
474
28.81/
20.9944
46.2085
23.79/ 0.7432/
/
22,
9006
446.0770
25.98/ 0,7618
21.4305 /4.44257
The ARM platform shows even more significant gains, with speedups of 1. 332-5.79xagaiiii Ba- VSC [3] and 1.07-1.53x x against SSL [42]. Notably, on UDM10 [45], our ARM implementation achieves a remarkable 5.79x speedup over Bas [3] while reducing peak memory by approx- imately 17% compared to SSL [42]. The flow-warping (alignment) operator runs in FP32 with lightweight quantization overhead, preserving reconstruction quality while maintaining efficiency. We observe that the ARM platform demonstrates superior scalability for our method, where the uni- fied memory architecture avoids NUMA penalties and pross-socket traffic overhead present in the dual-socket Intel system. These results demonstrate that our temporal awareness adaptation quan- tization approach delivers consistent acceleration and memory efficiency across diverse hardware architectures and datasets, while maintaining the reconstruction quality reported in the main paper.
Table 10: Inference latency and memory usage on x86 and ARM Platforms
Intel Xeon Gold 5218R
Apple M1 Pro
Dataset
method
Memory (MB)
Latency (s)
Speedup
Memory (MB)
Latency (s)
Speedup
BSsieVSR [3]
18823.56
2677.44
1.00x
10506.25
1293.22
1.00x
REDS4 [33]
SSL [42]
19047.57
2203.05
1.22x
10122.02
1009.10
1.28x
Ours-FQ
19068.67
1963.63
1.36x
10034.67
659,82
1.96x
BSsieVSR [3]
14811.98
1252.88
1.00x
11346. 16
3292. II
1.00x
UDMI0
[42]
15020. 86
909.86
1.38
10337.38
850.59
3.87x
Ours-FQ
15083.24
866.65
1.45
8549.67
568.83
x.79
BSR [3]
3688.39
2095.47
1.00x
4470.97
422.10
1.00x
Vid4 [30]
SSL(42)
4318.05
1860.86
1.13x
4511.02
340.19
1.24
Ours-FQ
3605.30
1631.11
1.28x
3435.47
318.79
1.32x
C Overall Algorithm
Our Temporal Awareness Adaptation Quantization for VSR is summarized in Alg 1.
D Related Work
We provide a detailed comparison with AdaBM [16] to further clarify the distinctions in method- ology and contributions. It is important to note that AdaBM was originally designed for image SR and introduced an efficient on- adaptive quantization framework that significantly reduces processing time. While we acknowledge the conceptual inspiration our work drew from AdaBM in terms of bit-level adaptation, we would like to emphasize that our framework is fundamentally redesigned to address the unique challenges of VSR, making a substantially different and purpose- built solution. The core technical divergence lies in the incorporation of temporal modeling. Ad-
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Algorithm 1 Temporal Awareness Adaptation Quantization for Video Super-Resolution
Input: Pretrained 32-bit VSR network P of K parameter-shared layers, calibration dataset Deal
1.W
[ViRIE (=1" spatial percentile parameter Pspaces temporal percentile parameter Premp-
Output: Quantized VSR network Q.
1: Calibration Phase:
2: Initialize weight clipping ranges {u k== using OMSE [8]:
1
3: Initialize activation clipping ranges {(g K
Ja with the EMA smoothed minimum and maxi-
mum statisties [20] by running calibration dataset through the FP network;
4: for i== = 1 to N do
5: Calculate the video flow-gratd Now-gradient complexity metric Cvideo (see Eq.\ (9)):
6: Calculate the spatial sensitivity { k=1
K
(see Eq.1 (10));
7: Calculate the temporal sensitivity { J=mmpk==1 k=1 (see Eq.\ (11));
8: end for
9: Initialize spatial thresholds Ispace and Mspace according to Pspace- th and (100 PPppce )-th percentiles
of the spatial sensitivity values { } k=1
10: Initialize temporal thresholds heep and Utmmp according to Piemp -th and (100- - Premp) )-th per-
centiles of the temporal sensitivity values {stemp) ) A==
11:
Initialize temporal shared layer bit adaptation factor {b) A==
k= (see Eq.1 (12));
12: Update activation clipping ranges {1h "
k=1 using OMSE [8] depending on the temporal K
shared layer bit adaptation factor {b[]]= assigned per layer;
13: Fine-tuning Phase:
14 14: Update weight clipping ranges {u R=1 using Eq.1 (16);
15: Update activation clipping ranges (1% " using Eq.. (16);
16: Update bit adaptive module parameters l,26, 4v26, {b) using Eq. (16);
17: Output the final quantized VSR model Q.
aBM, targeting single-image SR, relies solely on spatial sensitivity for bit allocation. In contrast, our method explicitly models spatio-temporal variance through components like FG-VBA and TS-LBA. This temporal awareness is critical for maintaining coherence across frames in VSR but was absent in AdaBM. Furthermore, the training strategies differ significantly due to the inherently higher com- putational cost of processing video sequences compared to single images. AdaBM's 's requirement for ten full epochs of fine-tuning, while feasible for image SR models, becomes prohibitively expensive for VSR. Our method addresses this by employing an efficient three-stage progressive optimization schedule. This approach updates parameters in distinct stages, progressing through weight clipping, activation clipping, and finally bit adaptation modules optimization, which enables fast convergence in merely epochs and demonstrates more stable optimization behavior, making it far more prac- tical for VSR model quantization. The design of the loss function also reflects our focus the temporal dimension. We extend beyond the spatial reconstruction loss used in image SR by intro- ducing temporal structure consistency loss. This addition is crucial for ensuring smooth transitions between frames and preventing artifacts like flickering, thereby enhancing the perceptual quality of the output video. Lastly, our approaches diverge in the handling of the bit space. Unlike AdaBM, which incorporates bit- bil-penalization loss to explicitly encourage lower bit-widths, we intentionally forgo such constraint. We found that an explicit penalization could lead optimization insta- bility in VSR quantization. Instead, our method allows the optimal bit allocation to emerge more naturally through a data-driven optimization process, which we found be more suitable for the complex spatio-temporal features in video data. In conclusion, while AdaBM provided valuable in- sights for adaptive quantization image SR, our framework introduces critical innovations, from its temporal-awart modules and efficient training strategy to its specialized loss design and the removal of explicit bit- benaliaai,,,,ion, that collectively constitute a novel and substantially different solution tailored for the demands of VSR.
19

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E More Visual Comparison
More visual results of the 4-bit FQ BasicVSR model are presented below. Our algorithm achieves the highest PSNR value and restores the most optimal results across all datasets. Moreover, after applying our designed FT strategy described in Sec 3.5, the performance of other benchmark algo- rithms is significantly improved, further validating the effectiveness of the proposed FT method.
F
Limitation and Future Direction
Currently, we only investigate quantization for video super-resolution. is better to generalize the quantized networks for other video restoration applications. Additionally, similar architectural con- siderations apply across various video processing tasks. These methodologies commonly eliminate Batch Normalization layers to maintain dynamic range adaptability while minimizing generative anomalies. Our ongoing research will include comprehensive empirical validation and investigate the broader applicability of this approach to pixel-to-pixel video tasks through systematic experi- mentation in subsequent studies.
G Code
We have provided code reproduce the results in this work.
20

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MinMax
Percenile
DBDC
MinMix IT 0.7729
0.73300
S 5880
Frnase 077, Clipp
Percentile ITT 25,80/ 0,7436
DODCIIT 09:60.7766
Ours 27.30 / 0.8241
GT PSNR/ SSIM
MinMax
Poreznile 28.17/08103
MiaMax+FT 0.337
025, Clip
Percetile-FF FT 0.8285
DBDC-FT
Ours 30.78 0.8838
70,8578
31.15/0.8885
31.31 0.88
32.54 /08888
Clip
Percenile-FF 0,5369
DEDC-FT
Ours 33.76 09177
GT
3..67:0.841
PSNR SSIM
35
JR
15
MinMax 28.08 0.8474
Peremile
DRDC 28.15 0.8524
MrnMox:FT 29.06 / 0.8741
27.48/ 0.876
12
3.2 //
3.5
M 4024 une
aaee arerov
Trume 062, Clip 020
Percentil 27.94 70.555
29.16 08784
30086 0.9062
PSNR: SSIM
Figure 3: Qualitative comparison on REDS4 [33] (Zoom-in for best view)
21

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cooL COOL
MinMax
Paremtile
MinMax+FT
COOL COOLCOOOOOOOOOOOOO COOL
COCL
Clip
Percenlic+EF 10,9644
DBDC+FT
Ours
GT
0.9730
MinMax 38.18 0,9951
Percentile
DEDC 38:37/ 99615
MinMax
0.9694
0.963
OIL OIL
Percenile+FF
DEDCHFT
Ours
GT
SSIM
MinMax 35.12.00.928 0.934
MinMax-FF 35,80 0 9364
38.73/0.8412
38.51 /0.932
D.A.
087, Clip
Percenile-KT
DEDC4FT
0.9444
simo
MinMax
Percentile
DRDC
MinMaxeFT 09.77099673
0.9633
3885 /0963
sino
ouUs
Frame 10%, Clip 0S8
Percentiee 39.10:0.9688
DODC 39.92/0..00 0.9697
Ours 41.f1 0099777
PSNR/SSIM
Figure 4: Qualitative comparison on Vimeo-900 [44] (Zoom-in for best view)
22

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28
28
28
MinMax 21.27/0.733
Percmiile
DRNC
MinMax+FF 0.763/0.7455
28
28
28
10
012, Calendar
Percanile=FT
DBDC=FT 22.05 22.05/0.76688
Ours 22.23 0.7898
GT PSNR SSIM
20.48/0,700 0,7003
MinMax 24.28 0,6086
Percenille
DBDC
MinMax TT 24.69 000883
24.13/0.662
24.33 00666
014,
Percemile-FF1
DEDCFFT
0.77213
MinMaa 23.46 0,6949
Percentlle
DBDC
MinMax-FT 21,05 007114
23.18:0.6782
33.81/0.71.0
028, Clip Foliage
Percemile-FF1 -FT
DBDC+FT
ManMax 17.44/0.850
Perezntlle
DBDC 28.00/0.399
MinMax+FF 28.10 0.8799
26.48/0.855 0.8558
Trame 020, Clip Walk
DRICHFT Figure 5: Qualitative comparison on Vid4 [30] (Zoom-in for best view)
Pereamile=FT 26.72/
23

---

MinMax
Persenille
DBDC
MinMax+FT 33.08/0.9583
02.79:09144
32.08 009334
Frame 026, Clip Arokpeople
Perventile 32.27/ 0.9361
DBDC
Ours 34.72/ 0.9570
GT PSNR / SSIM
34.14 1.14400.00 0.9424
Percentle
23.1110
27 1888
29:25-09941
28,85 0.8999
5J5
535
Frame 007, Clip andi
Pereentile-F7 27.37: 8848
DBDCHFT 29.38 0.9110
30.33 0.9318
PSNR / SSIM
CANO
CANO
CANO
CANO
MinMax CANO
Pereentlee 42.06 O
MInMEs+FT
0.97R
08.15/0..849
CANO
CANCI
CANCI
015, Camera
Percenall 0.9884
DBDCIFT
Ours
44.44 0.9854
45.48 A 0.9900
PSNR/ SSIM
MinMax+FFT 34.85/0.9500
03.63:09377
/ 0.9458
34.46 166600 04855
Clip Clap
Porsentil++F 32.57/0000
SSUM
Figure 6: Qualitative comparison on UDM10 [45] (Zoom-in for best view)
24

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Reviews:
[{"review_id": "oUIugY6tbd", "reviewer": "Reviewer_E16n", "summary": "The authors QBasicVSR: Temporal Awareness Adaptation Quantization for Video Super-Resolution. It is a quantized model based on BasicVSR for video super resolution. The author's main design focus is on the quantization bit depth. Specifically, QBasicVSR uses a dual adaptation strategy: flow-gradient video bit adaptation and temporal shared layer bit adaptation, optimizing bit-width allocation for both video streams and shared network layers. Additionally, they propose a fine-tuning method to refine the quantization parameters, supervised by the full-precision model. Experiments demonstrate that the proposed method outperforms comparative methods.", "questions": "1. Why is the comparison of resource and time consumption made between quantization methods and VSR methods, when the quantization method is based on the VSR method?\n2. Please include comparisons with more recent quantization methods that can be applied to VSR models. \n3. Clarify how the bit number \"w6a6\" in the experimental result table is obtained, since the quantization bit-width is dynamically optimized. Is it the average value or b_{base}? If it's the latter, what would the average bit be, and is it possible that the actual bit-width could be significantly larger than \"w6a6\" due to b_V and b_L^k remaining large?\n4. The video-wise bit adaptation factor b_{video} is dynamically determined during inference. How much computational load will this introduce? Is it acceptable for quantization?", "limitations": "The authors have discussed relevant limitations.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n1. The authors propose a dual adaptation strategy combining flow-gradient video bit adaptation and temporal shared layer bit adaptation to adapt bit-widths based on video complexity and layer sensitivity, effectively reducing quantization error.\n2. A fine-tuning strategy is introduced further to optimize bit allocation and clipping ranges under full-precision supervision.\n3. Testing on multiple datasets shows that the quantized model maintains high accuracy under low-bit quantization.\n4. The authors conduct thorough experiments on various modules in the ablation study, proving the effectiveness of each proposed method.\n\n### Weaknesses\n\n1. The comparison of quantization methods with VSR's training time and resource consumption is strange (Table 4). The claim in the article that it is 200 times faster than SSL is confusing.\n2. The methods compared with static quantization are outdated (Minmax-2018, Percentile-2019). There is a lack of comparison with more recent quantization methods, e.g., DBDC+Pac, SVDQuant, and SmoothQuant.\n3. As L239 mentions, the bit count for different videos is dynamically determined, which may be unfriendly for actual deployment on hardware.", "was_revised": true, "final_justification": "The proposed method is well-designed and effective. Moreover, the authors have resolved my concerns during the rebuttal phase. I think the paper deserves a score of 5.", "rebuttal": "**Response to Reviewer E16n - Question 1 (Comparison with SSL):**\n\n**Thank you for raising this point. We agree that comparing quantization methods directly with core VSR methods in terms of training time/resource might seem unusual at first glance. However, the intention of Table 4 is specifically to compare the efficiency of different VSR model compression techniques applied to pretrained VSR models, not to compare quantization against the core VSR training itself. They both belong to efficient VSR methods.**\n\n**1. SSL as a Compression Baseline:** SSL is a VSR model compression technique applied to pretrained VSR models. Its compression process (pruning + fine-tuning the pre-trained VSR model) is reported to require **8 A6000 GPUs for 370 hours**.\n\n**2. Our Method as a Quantization Compression:** Similarly, our quantization method is a *model compression technique* applied to the same pretrained VSR models. Our quantization process requires only **1 A6000 GPU for 105 minutes**.\n\n**3.Revision Plan:** We will clarify this intent explicitly in the revised manuscript, emphasizing that the comparison is between *existing SOTA compression techniques for VSR models*, not between quantization and core VSR training. We will also explicitly show the time calculation (370 hrs -> 22,200 min / 105 min ≈ 211x) for transparency.\n\n**Response to Reviewer E16n - Question 2 (Comparison with Recent Quantization Methods):**\n\n**We appreciate the reviewer’s valuable suggestion. We agree that recent quantization methods provide more meaningful baselines. Accordingly, we have added a comparison with DBDC, which is one of the few recent methods explicitly designed for low-bit quantization in super-resolution (SR) tasks. DBDC shares a similar target with our approach and provides a more relevant reference than earlier static quantization methods.**\n\nRegarding **SVDQuant** and **SmoothQuant**, while they are indeed recent and impactful quantization approaches, **they are not directly applicable to SR models**, especially those with **flow-guided temporal propagation** in VSR. Specifically:\n\n***SVDQuant** is designed around SVD-based matrix decomposition and is typically applied to classification models. Its assumptions and compression pipeline are not tailored for the unique challenges of SR, such as spatial fidelity and temporal consistency.\n\n***SmoothQuant** relies on *cross-layer fusion* and assumes the presence of skip-connections across layers. In SR architectures like BasicVSR, particularly within the propagation modules, such skip-connections are structurally absent or non-trivial due to warping and alignment stages. Thus, **direct application is not possible** without **additional architecture modification or fine-tuning**, which would go beyond the scope of a fair out-of-the-box comparison.\n\nFurthermore, as highlighted in the paper “Toward Accurate Post-Training Quantization for Image Super Resolution”, SR tasks pose **unique difficulties for quantization**, including:\n\n- Highly dynamic activation ranges\n- Asymmetric and long-tailed distributions\n- Absence of normalization layers like BatchNorm\n\nThese properties make the direct transfer of classification-oriented quantization or NLP quantization methods (e.g., SVDQuant or SmoothQuant) not only ineffective but also **potentially misleading** unless carefully adapted and re-trained. In fact, even in the DBDC paper, the authors only compare against Minmax and Percentile methods — reinforcing the lack of universally accepted strong quantization baselines for SR.\n\n**In summary**, we intentionally focus on comparing with DBDC as the most relevant and compatible recent method for SR. Including SVDQuant or SmoothQuant would require non-trivial adjustments and retraining under our method’s supervision, potentially biasing the comparison or altering their original design intents.\n\n|Methods| FQ    | W/A       | Process Time | REDS4 (BI)               | Vimeo90K-T (BI)          | Vid4 (BI)                | UDM10 (BD)               | Vimeo90K-T (BD)          | Vid4 (BD)                |\n| ----------------- | ----- | --------- | ------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ |\n| BasicVSR-DBDC     | ✓     | 4/4       | 30min        | 28.07 / 0.7817 / 4.0     | 35.23 / 0.9225 / 4.0     | 26.24 / 0.7720 / 4.0     | 35.96 / 0.9339 / 4.0     | 34.77 / 0.9172 / 4.0     | 26.01 / 0.7581 / 4.0     |\n| BasicVSR-DBDC+FT  | ✓     | 4/4       | 95min        | 29.22 / 0.8223 / 4.0     | 35.45 / 0.9243 / 4.0     | 26.31 / 0.7703 / 4.0     | 36.82 / 0.9434 / 4.0     | 35.25 / 0.9228 / 4.0     | 26.42 / 0.7764 / 4.0     |\n| **BasicVSR-Ours** | **✓** | **4/4MP** | **90min**    | **30.26 / 0.8637 / 5.5** | **35.82 / 0.9311 / 5.0** | **26.29 / 0.7752 / 4.7** | **37.59 / 0.9536 / 4.8** | **35.95 / 0.9339 / 5.1** | **26.81 / 0.8025 / 5.0** |\n\nDue to space limitations, we only report the **4-bit full quantization results** in all the tables. \n\n**Response to Reviewer E16n - Question 3 (The bit number):**\n\n**We appreciate the reviewer’s detailed question.** The reported bit-width configuration such as \"w6a6\" refers specifically to the base bit-width $b_{base}$, which defines the initial quantization level before applying dynamic adaptation. For example, in the \"w4a4\" case, this means both weights and activations are initialized at 4-bit.\n\nTo clarify how the final bit-widths are derived, our fine-tuning process consists of **three sequential optimization stages**:\n\n1. **Stage 1**: Optimize **weight clipping parameters** while freezing activations and bit adaptation parameters.\n2. **Stage 2**: Optimize **activation clipping parameters**, with others still fixed.\n3. **Stage 3**: Optimize **bit adaptation parameters**, enabling more precise dynamic bit-width adjustment during inference.\n\nEach stage independently fine-tunes a subset of parameters, while keeping others fixed, and the entire process completes in only **three epochs**.\n\nDue to the dynamic nature of our method, the actual bit-width per layer may deviate from the base setting. Importantly, we emphasize that even in **Stage 1 and 2**, our model already **outperforms existing baselines** (e.g., MinMax, Percentile, DBDC) **using only low feature average bit-width (FAB)** that is averaged throughout the video clips of the test dataset. The bit adaptation modules are present across all stages, but they are **only optimized during Stage 3**, where we enable the model to learn bit allocation based on content and layer dynamics. This further improves performance at the cost of a **slightly higher average bit-width** (e.g., up to 5.5 on REDS4), offering a tunable trade-off between precision and efficiency depending on deployment constraints.\n\nEven after **only Stage 1**, our method **already outperforms existing baselines** (e.g., MinMax, Percentile, DBDC) in both accuracy and average bit cost. This design allows flexibility:\n\n- If **low-bit efficiency** is prioritized, one may adopt the results from **Stage 1 or 2**.\n- If **higher performance** is desired, **Stage 3** offers further improvement with a modest increase in average bit-width.\n\nThis staged approach **demonstrates the dynamic adaptability** of our framework and offers a tunable trade-off between compression and performance.\nNotably, the bit-width of weights remains fixed at 4 bits throughout all stages in \"w4a4\" and does not dynamically change; only the bit-width of activations is dynamically adapted.\n\n| Methods          | FQ    | W/A     | Process Time | REDS4 (BI)                | Vimeo90K-T (BI)           | Vid4 (BI)                 | UDM10 (BD)                | Vimeo90K-T (BD)           | Vid4 (BD)                 |\n| ---------------- | ----- | ------- | ------------ | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- |\n| BasicVSR-DBDC     | ✓     | 4/4       | 30min        | 28.07 / 0.7817 / 4.0     | 35.23 / 0.9225 / 4.0     | 26.24 / 0.7720 / 4.0     | 35.96 / 0.9339 / 4.0     | 34.77 / 0.9172 / 4.0     | 26.01 / 0.7581 / 4.0     |\n| **Ours-Stage1**  | **✓** | **4/4** | **40min**    | **29.82 / 0.8466 / 3.98** | **35.46 / 0.9266 / 3.95** | **26.12 / 0.7666 / 3.75** | **36.69 / 0.9464 / 3.70**  | **35.45 / 0.9272 / 3.95** | **26.32 / 0.7716 / 3.75** |\n| **Ours-Stage2**  | **✓** | **4/4** | **65min**    | **29.86 / 0.8479 / 3.98** | **35.51 / 0.9270 / 3.95** | **26.21 / 0.7695 / 3.75** | **36.76 / 0.9471 / 3.70**  | **35.54 / 0.9283 / 3.95** | **26.38 / 0.7741 / 3.75** |\n| **Ours-Stage3**  | **✓** | **4/4** | **90min**    | **30.26 / 0.8637 / 5.50** | **35.82 / 0.9311 / 5.03** | **26.29 / 0.7752 / 4.69** | **37.59 / 0.9536 / 4.78** | **35.95 / 0.9339 / 5.08** | **26.81 / 0.8025 / 4.98** |\n\n**Response to Reviewer E16n - Question 4 (Computation load):**\n\nThe computation of the video-wise bit adaptation factor $b_{video}$ introduces **negligible computational overhead**, as it is performed **once per video** based on simple statistical properties extracted during a forward pass. This process does **not require iterative optimization**, and is implemented with minimal additional cost.\n\nTo empirically validate the efficiency of our approach, we report the processing time required for different methods under the 4-bit full quantization setting above. Our method achieves **faster processing** compared to DBDC+FT across all stages. For instance:\n\n- Even **our full-stage method** (Stage 3), which includes bit adaptation parameters optimization, completes in only **90 minutes**, and outperforms DBDC+FT in both speed and accuracy.\n\nThis clearly demonstrates that the integration of $b_{video}$ remains efficient and practical for deployment, and does not introduce any prohibitive computational load. **We thank the reviewer once again for these valuable comments.**"}, {"review_id": "lyBEifvseR", "reviewer": "Reviewer_gBYd", "summary": "The paper introduces QBasicVSR, the first post-training quantization (PTQ) framework tailored for video super-resolution (VSR). It identifies three challenges unique to VSR: temporal error propagation, shared temporal parameterization, and temporal metric mismatch. To addresses these problems, temporal awareness adaptation post-training quantization (PTQ) is introduced, which comprises two complementary modules: (1) Flow-Gradient Video Bit Adaptation (FG-VBA) computes a spatiotemporal complexity metric over bidirectional optical flows and image gradients to adapt the global bit-width for an input video; (2) Temporal Shared Layer Bit Adaptation (TS-LBA) analyzes per-layer spatial and temporal sensitivities from activation statistics to assign layer-wise bit-width factors. These modules are initialized via a small set of unlabeled calibration clips and then jointly fine-tuned under supervision of the full-precision model, using both pixel and feature-transfer losses.", "questions": "The experiments can be further enhanced. Please see weakness for details.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \nS1-This paper proposes a well-motivated dual adaptation framework: Flow-Gradient Video Bit Adaptation (FG-VBA) and Temporal Shared Layer Bit Adaptation (TS-LBA), which are rigorously derived and mathematically formalized.\nS2-The methodology is explained with clear diagrams (e.g., Figure 1) and detailed formulas that clarify the inner workings of FG-VBA and TS-LBA.\nS3-Speedup (>×200 faster vs. SSL) and resource reduction (1 GPU vs. 8 GPUs) make the work highly relevant for practical large-scale video applications.\n\nweaknesses:\nW1. Experiments only target BasicVSR and its lite version. Can this prove the generalizability of the quantification method? For example, the following articles verify the effectiveness of the quantization method on multiple existing networks.\n[1] Content-Aware Dynamic Quantization for Image Super-Resolution.\n[2] FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer\n[3] AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution\n\nW2. Real-world evaluation (e.g., on actual hardware or mobile deployment scenarios) is missing. \n\nW3. Some parameters, such as l_v2b, u_v2b, l_space and u_space, depend on percentage thresholds. Is it necessary to determine different initial statistical thresholds for different datasets?\n\nW4. Is it possible to add real-world video super-resolution evaluation instead of videos from the dataset?\n\nW5. In Flow-Gradient Video Bit Adaptation (FG-VBA), how does C_video map to the global bit width factor b_video? \n\nW6. In Temporal Shared Layer Bit Adaptation (TS-LBA), spatial sensitivity s_space^k computes the standard deviation across the channel dimension (dim=1). Is dim=2?", "was_revised": true, "final_justification": "According to the rebuttal, I'll maintain my positive rating.", "rebuttal": "**Response to Reviewer gBYd - Question 1: the Generalizability of the Quantification Method :**\n\n**We thank the reviewer for pointing out the importance of evaluating generalizability across diverse model architectures.** To address this point, we have conducted **additional experiments** beyond the original BasicVSR, including retraining a **unidirectional variant of BasicVSR** (denoted as *BasicVSR-uni*, where the backward propagation branch is removed). This setup provides a significantly different architectural structure while retaining the core super-resolution logic.\n\nAs shown in the updated results table below, **our quantization method maintains strong performance** across this newly introduced architecture. Even under full quantization, our approach consistently achieves competitive or superior results compared to full-precision baselines and other efficient VSR models, demonstrating the **robustness and adaptability** of our method to different network variants.\n\n|Methods|W/A|FQ|Params(M)|GT|REDS4 (BI)|Vimeo-90K-T (BI)|Vid4 (BI)|UDM10 (BD)|Vimeo90K-T (BD)|Vid4 (BD)|\n| ------------- | --------- |----| --------- | ---- | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ |\n|BasicVSR-uni|32 / 32|-|2.6|✓|30.54 / 0.8694|36.99 / 0.9429|27.03 / 0.8163|39.29 / 0.9646|37.27 / 0.9473|27.54 / 0.8419|\n|BasicVSR-uni-lite|32 / 32|-|0.7|✓|29.95 / 0.8561|36.38 / 0.9372|26.68 / 0.8012|38.24 / 0.9586|36.38 / 0.9388|26.87 / 0.8157|\n|L1-norm-uni|32 / 32|-|0.7|✓|29.97 / 0.8570|36.45 / 0.9381|26.70 / 0.8031|38.43 / 0.9601|36.53 / 0.9405|26.89 / 0.8187|\n|ASSL-uni|32 / 32|-|1.3|✓|30.02 / 0.8589|36.49 / 0.9385|26.76 / 0.8051|38.48 / 0.9603|36.61 / 0.9416|27.02 / 0.8236|\n|SSL|32 / 32|-|0.5|✓|30.24 / 0.8633|36.56 / 0.9392|27.01 / 0.8148|38.68 / 0.9615|36.77 / 0.9429|27.18 / 0.8296|\n|**Ours**|**6 / 6**|✓|**0.55**|✗|**30.35 / 0.8646**|**36.63 / 0.9396**|**26.81 / 0.8060**|**38.70 / 0.9610**|**36.83 / 0.9434**| **27.29 / 0.8310** |\n|**Ours**|**6 / 6**|✗|**0.8**|✗|**30.40 / 0.8665**|**36.81 / 0.9413**|**26.91 / 0.8101**|**38.97 / 0.9633**|**37.11 / 0.9460**|**27.40 / 0.8372**|\n\nTo further validate the effectiveness of our quantization strategy itself, we conducted a direct comparison with other standard quantization methods under the same network setting — using BasicVSR-uni as a shared backbone. \n\n|Methods|FQ|W/A|Process Time|REDS4 (BI)|Vimeo90K-T (BI)|Vid4 (BI)|UDM10 (BD)|Vimeo90K-T (BD)|Vid4 (BD)|\n| ---------------------- | ---- | ----- |----- | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ |\n|BasicVSRuni|-|32/32|-|30.54 / 0.8694|36.99 / 0.9429|27.03 / 0.8163|39.29 / 0.9646|37.27 / 0.9473|27.54 / 0.8419|\n|BasicVSRuni-MinMax|✓|4/4|4min|28.45 / 0.7997|34.31 / 0.9088|25.56 / 0.7308|35.53 / 0.9274|34.55 / 0.9142|25.82 / 0.7531|\n|BasicVSRuni-Percentile|✓|4/4|3min|28.12 / 0.7984|33.94 / 0.9096|24.81 / 0.7030|34.76 / 0.9318|33.97 / 0.9111|24.87 / 0.7138|\n|BasicVSRuni-MinMax+FT|✓|4/4|35min|28.86 / 0.8135|34.70 / 0.9146|25.72 / 0.7384|36.33 / 0.9392|34.88 / 0.9182|26.14 / 0.7667|\n|BasicVSRuni-Percentile+FT|✓|4/4|30min|28.35 / 0.8074|34.03 / 0.9107|24.92 / 0.7105|35.05 / 0.9341|34.06 / 0.9125|25.09 / 0.7242|\n|**BasicVSRuni-Ours**|**✓**|**4/4MP**|**35min**|**29.55 / 0.8434**|**35.75 / 0.9296**|**26.15 / 0.7674**|**37.29 / 0.9517**|**35.72 / 0.9319**|**26.53 / 0.7938**|\n\nThese findings confirm that our quantization strategy generalizes well across multiple network forms, thereby addressing your concern about architecture-specific overfitting.\n\n**Response to Reviewer gBYd - Question 2: Real-world evaluation :**\n\n**We thank the reviewer for highlighting the importance of real-world deployment evaluation.** \nTo address your concern, we conducted inference latency and memory footprint measurements on two representative hardware platforms: an x86-based Intel Xeon CPU and an ARM-based Apple M1 Pro chip. To simulate realistic deployment scenarios, the evaluation was performed with batch size set to 8 and input resolution of 180×320, which reflects a typical low-resolution video processing workload.\nThe detailed results are provided below, which demonstrate that our quantized model achieves significant reductions in inference time and memory usage compared to the full-precision BasicVSR and the strong SSL baseline. \n\n|Methods|BasicVSR|BasicVSR-SSL|BasicVSR-Ours|\n|---------------|------------------|------------------------|--------------------------|\n|**x86 CPU**|31.59 sec|23.00 sec (×1.373)|14.01 sec (×2.255)|\n| |11847.04 MB|6029.70 MB (×1.965)|7331.03 MB (×1.616)|\n|**ARM CPU**|61.79 sec|18.03 sec (×3.427)|18.89 sec (×3.271)|\n| |10725.58 MB|9207.39 MB (×1.165)|7029.81 MB (×1.526)|\n\nWe also note that current inference libraries primarily support INT4/INT8 acceleration, so intermediate bit-widths are upcasted to INT8 for compatibility. Despite this limitation, our method already demonstrates improved efficiency. We anticipate further gains once native support for intermediate-bit-width quantization becomes more widely available.\nOverall, these results clearly demonstrate the deployment potential of our quantized framework, showcasing substantial improvements in inference latency and memory usage under realistic settings.\n\n**Response to Reviewer gBYd - Question 3: Parameter Threshold Generality :**\n\n**We thank the reviewer for raising the question regarding the use of threshold-based parameters such as** $l_{v2b}$, $u_{v2b}$, $l_{space}$ and $u_{space}$.\n**In all our experiments, we adopt a fixed set of threshold values, which are shared across all datasets, models, and training stages. These values were not specifically tuned for any individual dataset.** We maintain consistent settings as described in the experimental details, without performing dataset-specific fine-grained calibration.\nThis design decision highlights an important strength of our method: it achieves strong and stable performance across diverse benchmarks without requiring extensive hyperparameter search or specific adjustments. Such robustness underscores the generalizability and practicality of our approach.\n\n**Response to Reviewer gBYd – Question 4: Real-world Video Evaluation**\n\n**We appreciate the reviewer’s suggestion regarding evaluation on real-world video content.**\nIn principle, our framework can be directly applied to real-world videos. However, since real-world footage lacks corresponding high-quality ground truth (GT) references, commonly used full-reference metrics such as PSNR and SSIM cannot be computed. In such cases, only perceptual quality metrics (e.g., LPIPS, NIQE) or user studies can be used for evaluation.\nTo maintain consistency and ensure fair, quantitative comparisons with prior work, we adopt standard datasets where GT is available. In other words, our framework is model-agnostic and fully compatible with arbitrary video input. Users may feed any real-world video through our quantized models to visually inspect results or evaluate using perceptual metrics.\n\n**Response to Reviewer gBYd – Question 5: Mapping from** $C_{video}$ **to** $b_{video}$\n\n**We thank the reviewer for the question regarding how the global bit-width factor** $b_{video}$ **is derived from video complexity** $C_{video}$ **in our Flow-Gradient Video Bit Adaptation (FG-VBA) module.**\n\nTo clarify: the thresholds $l_{v2b}$ and $u_{v2b}$ are **computed only once during the calibration phase**, using a set of 100 video clips, which is called calibration dataset. We calculate the 100 complexity scores $C_{video}$ for these video clips, and determine:\n\n$l_{v2b}$ as the $p_V$-th percentile of the $C_{video}$ distribution\n\n$u_{v2b}$ as the ($100 − p_V$)-th percentile of the $C_{video}$ distribution\n\nThis process is called calibration phase. Then QBasicVSR fine-tune $l_{v2b}$ and $u_{v2b}$ in our fine-tuning phase.\nThis statistical analysis sets **fixed bounds** that remain **unchanged during inference.** \nDuring inference, when **a new video** is processed, its $C_{video}$ is first computed. Then, the global bit-width adjustment factor $b_\\text{video} \\in \\{-1, 0, +1\\}$ is assigned as follows:\n\nIf $C_{video}$ > $u_{v2b}$, the video is considered complex, and we set $b_{video} = +1$ to increase bit-widths and preserve fidelity.\n\nIf $C_{video}$ < $l_{v2b}$, the video is deemed simple, and we set $b_{video} = −1$ to reduce bit-widths and save computation.\n\nOtherwise, we keep $b_{video} = 0$, using the default configuration.\nIn this way, our framework **automatically adapts bit-widths for unseen videos**, using a content-aware rule established from the calibration dataset. This strategy ensures generalization across inputs without manual tuning per video.\n\n**Response to Reviewer gBYd – Question 6: Clarification on Spatial Sensitivity Dimension**\n\n**We sincerely thank the reviewer for the careful reading and for pointing out the discrepancy in our description of the spatial sensitivity computation in the Temporal Shared Layer Bit Adaptation (TS-LBA) module.**\n\n**You are absolutely correct** — the standard deviation for the spatial sensitivity term $s_{space}^k$ is computed along the spatial dimension (dim=2), not the channel dimension as mistakenly written. This was a misstatement in the textual description, and we apologize for any confusion it may have caused.\nTo clarify, the correct computation aggregates standard deviation across the spatial locations of the feature map to measure spatial variance per channel, which then guides the shared bit allocation logic. The implementation is correct and consistent with our intended design, only the textual description contains the incorrect reference to dimension.\nWe will ensure this is corrected in the final version of the paper.  **We sincerely thank the reviewer again for identifying this issue and contributing to the clarity and accuracy of our manuscript.**"}, {"review_id": "qpE156wQnl", "reviewer": "Reviewer_zMGT", "summary": "The authors proposed a new solution for quantizing video super-resolution (VSR) models. Inspired by AdaBM, a PTQ method for image super-resolution, the authors extended it to VSR by changing image complexity to image-bit mapping (AdaBM) to video complexity to video-bit mapping and spatial sensitivity-based layer-to-bit mapping (AdaBM) to spatio-temporal sensitivity-based layer-to-bit mapping. The method is applied to a single VSR baseline, BasicVSR, and experimental results support its effectiveness.", "questions": "In addition to the major concerns outlined above, several minor issues remain:\n1.\tExperimental results using different numbers of calibration images would help clarify the trade-off between training complexity and image quality.\n2.\tBeyond the standard PTQ baselines such as MinMax and Percentile, stronger baseline methods should be included for comparison, e.g., AdaRound and AdaQuant. The method proposed in [R1], as well as others reviewed therein, could also be considered.\n  [R1] Pushing the Limit of Post-Training Quantization, TPAMI 2025.\n3.\tThe main results in Table 1 should include variations of the proposed method with different bit configurations to provide a clearer view of performance under different settings.\n4.\tThe layer-wise and frame-wise bit adaptation results are neither analyzed nor visualized. Reporting only the overall average PSNR and SSIM makes it difficult for readers to understand the internal behavior of the adaptive bit allocation process.", "limitations": "The authors have clearly discussed the limitations of their work.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n\nAs the authors claim, the proposed method can be considered the first PTQ approach for VSR. The designs of the two key modules, flow-gradient video bit adaptation and temporal shared layer bit adaptation, are technically sound, and their effectiveness is validated through ablation studies.\n\nWeaknesses\n\n1. The proposed method is a relatively straightforward extension of AdaBM to VSR, as noted in the Summary section. In addition to reusing its core ideas, the paper closely follows much of the AdaBM paper’s structure, including its organization, figures, and equations.\n2. Given that this is the first PTQ work for VSR, readers may expect more than a direct adaptation of a prior method; they would look for task-specific motivations. For instance, early quantization studies in image super-resolution began with empirical analyses of activation distributions, which provided a clear rationale for novel quantization strategies. In contrast, the authors briefly mention three VSR-specific challenges in Lines 51–56 but do not support them with any theoretical or empirical analysis.\n3. As a result, the proposed method in its current form appears insufficiently tailored to VSR. Rather than briefly noting this as a limitation, the authors should consider applying their method to additional video processing tasks to demonstrate broader applicability.\n4. The method is evaluated only on a single VSR baseline [3]. To demonstrate generalization capability, it should be tested on multiple baselines, especially since the method involves at least seven empirically chosen hyperparameters, as noted in Lines 285–287.", "was_revised": true, "final_justification": "After reviewing the other reviewers' comments and the authors' feedback, I still find that the proposed method is not fully justified. A major concern shared by most reviewers is the limited generalization ability, as the method was evaluated only on BasicVSR. Although the authors provided additional results using BasicVSRuni during the rebuttal phase, it is essentially a simplified version of BasicVSR. The authors emphasize their contributions over AdaBM; however, some level of modification is always expected when extending a single-image method to video frames. From my perspective, the claimed contributions still appear relatively straightforward. Furthermore, the authors did not provide sufficient theoretical or empirical analysis to support the proposed methodology, relying primarily on performance results. Therefore, I keep my original rating.", "rebuttal": "**Response to Reviewer zMGT – Question 1: On the Novelty and Differences from AdaBM**\n\n**We sincerely thank the reviewer for raising this important point about the relation between our method and AdaBM.** While we did briefly mention AdaBM in the related work section, we **acknowledge that the distinctions were not sufficiently emphasized in the main paper**. This was not due to an intention to downplay its relevance, but rather because we initially focused on presenting the core design of our approach within the limited space. We appreciate the reviewer’s suggestion and are happy to clarify the differences in detail below.\n\n**Although our work draws conceptual inspiration from AdaBM**, particularly in the idea of bit allocation, we would like to emphasize that **our method is not a direct extension**. We introduce several critical innovations specifically tailored for video super-resolution (VSR), which fundamentally change the way the method operates.\n\nBelow, we highlight the key innovations that we believe are **crucial to the success of our method in the VSR setting**, and which fundamentally differentiate it from AdaBM.\n\n**1. Temporal-Aware Adaptation (Core Technical Extension)**\nWhile AdaBM targets single-image super-resolution and utilizes spatial sensitivity, our method incorporates **temporal modeling** through FG-VBA and TS-LBA. These components explicitly capture **spatio-temporal variance**, which is critical in VSR but absent in AdaBM.\n\n**2. Customized Finetuning Strategy (Training Efficiency)**\n\nAdaBM requires **10 full epochs of fine-tuning**, which is computationally affordable for image SR. However, this is **prohibitively expensive in VSR**, where input sequences are longer and the cost per epoch is significantly higher.\n\nIn contrast, our method adopts a **3-stage progressive optimization schedule**, where each stage updates only one group of parameters:\n\n- Stage 1: Weight clipping\n- Stage 2: Activation clipping\n- Stage 3: Bit adaptation\n\nThis staged approach offers:\n\n- **Fast convergence in only 3 epochs**\n- **Stable optimization behavior**\n- **Clear attribution of effects to each component**,\n\nmaking it far more practical for PTQ in video models.\n\n**3. Task-Specific Loss Design (Temporal Structure Awareness)**\n\nAdaBM primarily uses a spatial reconstruction loss. We extend this with a **temporal structure consistency loss**, encouraging smooth transitions across frames to prevent flickering and preserve perceptual coherence.\n\n**4. Flexible Bit Space (No Bit Penalization Loss)**\n\nAdaBM includes a **bit-penalization loss** to explicitly push bit-widths lower. We intentionally **do not include such a loss**, as we found it harms convergence and stability in video quantization. Instead, our method allows bit adaptation to **emerge naturally** through data-driven optimization, without explicitly penalizing higher bits.\n\nThis avoids unstable optimization trajectories that may result from over-aggressive bit constraints, especially in temporally variant features across video frames.\n\n**Conclusion**\n\nWhile AdaBM inspired our initial direction, the **core design of our method is purpose-built for VSR**. From temporal-aware adaptation modules to efficient optimization and loss design, our contributions form a **substantially different and VSR-specific framework**, going far beyond a simple adaptation.\n\n**We will include a detailed comparison with AdaBM in the supplementary material to further clarify the differences in design details and implementations. We sincerely thank the reviewer for pointing out this important issue and for the opportunity to clarify our contributions.**\n\n**Response to Reviewer zMGT – Question 2: Calibration Videos Trade-off**\n\n**We thank the reviewer for this suggestion.** To address your concern, we conducted an additional study varying the number of calibration video clips to assess the trade-off between calibration set size, training time, and final quality. As shown in the table below, increasing the number of calibration video clips generally improves performance up to a point, but the gain saturates beyond 100 video clips while the processing time continues to grow significantly. We therefore selected **100 video clips** as a practical compromise between efficiency and accuracy in all our experiments. The evaluation results are reported on the REDS4 dataset.\n\n| num     | FQ    | W/A     | Process Time | PSNR      | SSIM       |\n| ------- | ----- | ------- | ------------ | --------- | ---------- |\n| 32      | ✓     | 4/4     | 48min        | 29.97     | 0.8512     |\n| 64      | ✓     | 4/4     | 67min        | 30.11     | 0.8565     |\n| **100** | **✓** | **4/4** | 90min       | **30.26** | **0.8637** |\n| 128     | ✓     | 4/4     | 146min       | 30.14     | 0.8582     |\n| 160     | ✓     | 4/4     | 190min       | 29.87     | 0.8512     |\n\n**Response to Reviewer zMGT – Question 3: On the Choice of PTQ Baselines and Stronger Methods**\n\n**We thank the reviewer for the constructive comments.** To further assess generalization, we additionally evaluated our method on **BasicVSRuni**, a widely used variant of BasicVSR with the backward propagation branch removed to reduce computation cost (also adopted in prior studies such as SSL). Our method again outperforms MinMax, Percentile, and **DBDC (Toward Accurate Post-Training Quantization for Image Super Resolution, CVPR’23)** across multiple datasets.\n\nRegarding AdaRound, AdaQuant, and [R1]*Pushing the Limit of Post-Training Quantization* (TPAMI 2025), these approaches are primarily designed for classification or static image tasks and are not directly compatible with recurrent, flow-guided VSR architectures. **Previous PTQ works for image SR (e.g., DBDC, AdaBM)** also used MinMax and Percentile as baselines for fairness, so we followed the same protocol. **We will add a proper citation to [R1] (*Pushing the Limit of Post-Training Quantization*, TPAMI 2025) in the introduction** to acknowledge it as a **recent breakthrough in PTQ research**, while noting that **post-training quantization for video super-resolution has not been explored before our work**.\n\n| Methods | FQ   | W/A  | Process Time | REDS4 (BI) | Vimeo90K-T (BI) | Vid4 (BI) | UDM10 (BD) | Vimeo90K-T (BD) | Vid4 (BD) |\n| ------- | ---- | ---- | ------------ | ---------- | --------------- | --------- | ---------- | --------------- | --------- |\n|BasicVSR-DBDC+FT|✓|4/4|95min|29.22 / 0.8223|35.45 / 0.9243| 26.31 / 0.7703|36.82 / 0.9434|35.25 / 0.9228|26.42 / 0.7764|\n|**BasicVSR-Ours**|**✓**|**4/4MP**|**90min**|**30.26 / 0.8637**|**35.82 / 0.9311**|**26.29 / 0.7752**|**37.59 / 0.9536**|**35.95 / 0.9339**|**26.81 / 0.8025**|\n\n**Response to Reviewer zMGT – Question 4: on Table 1 Bit Configurations**\n\n**We thank the reviewer for this helpful suggestion.** We will revise Table 1 in the final version to present our method under different bit configurations for improved clarity. The corresponding experimental data already exist in the main paper and supplementary material, no new data are generated, and we omit them here to save space.\n\n**Response to Reviewer zMGT – Question 5: On Bit Adaptation Analysis**\n\n**We thank the reviewer for this helpful suggestion.** We will revise Table 1 in the final version to present our method under different bit configurations more clearly.\n\nDue to space constraints, we do not repeat the results of other baseline methods here, as they are already included in the main paper. If the you are interested in the raw numbers, we kindly refer to those tables for detailed comparisons. Here, we provide the feature average bit-width (FAB) results of our method across three stages for clarity which is is averaged throughout the video clips of the test dataset. Dynamic adaptation applies only to activations; weight bit-widths stay fixed. Notably, **even in Stage 1, our method achieves competitive or superior performance to baselines with lower bit-width usage**, demonstrating the effectiveness of our approach. \n\n|Methods|FQ|W/A|Process Time|REDS4 (BI)|Vimeo90K-T (BI)|Vid4 (BI)|UDM10 (BD)|Vimeo90K-T (BD)|Vid4 (BD)|\n| ---------------------- | ---- | ----- |----- | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ |\n|**Ours-Stage1**|**✓**|**4/4**|**40min**|**29.82 / 0.8466 / 3.98**|**35.46 / 0.9266 / 3.95**|**26.12 / 0.7666 / 3.75**|**36.69 / 0.9464 / 3.7**|**35.45 / 0.9272 / 3.95**|**26.32 / 0.7716 / 3.75**|\n|**Ours-Stage2**|**✓**|**4/4**|**65min**|**29.86 / 0.8479 / 3.98**|**35.51 / 0.9270 / 3.95**|**26.21 / 0.7695 / 3.75**|**36.76 / 0.9471 / 3.7**|**35.54 / 0.9283 / 3.95**|**26.38 / 0.7741 / 3.75**|\n|**Ours-Stage3**|**✓**|**4/4**|**90min**|**30.26 / 0.8637 / 5.50**|**35.82 / 0.9311 / 5.03**|**26.29 / 0.7752 / 4.69**|**37.59 / 0.9536 / 4.78**|**35.95 / 0.9339 / 5.08**|**26.81 / 0.8025 / 4.98**|\n\n**Response to Reviewer zMGT – Question 6: Generalization Capability and other questions:**\n\n**We thank the reviewer for this helpful suggestion.** We have additionally validated our method on **BasicVSRuni** (a variant of BasicVSR without the backward branch) to assess generalization, achieving **consistent improvements over MinMax, Percentile, and DBDC+Pac baselines**, confirming that our approach generalizes well beyond a single VSR model. **Due to space limitations, we omit the detailed table here, but if you are interested, we can provide it in the following discussion. VSR introduces unique PTQ challenges that are harder to visualize than static image SR. We have reported FAB per epoch above to show adaptive behavior.** Our framework is also applicable to other pixel-to-pixel video restoration tasks (e.g., denoising, deblurring), but large-scale PTQ experiments across multiple tasks are extremely time-consuming in videos, and we plan to investigate this in future work. **We sincerely thank the reviewer again.**"}, {"review_id": "fl4dxSw1WL", "reviewer": "Reviewer_45MJ", "summary": "The paper introduces QBasicVSR, the first post-training quantization (PTQ) framework specifically designed for video super-resolution (VSR), addressing unique challenges such as temporal error propagation and shared temporal parameters that hinder the direct application of image SR quantization methods. QBasicVSR employs a dual adaptation strategy: Flow-Gradient Video Bit Adaptation (FG-VBA) for video complexity-aware bit allocation, and Temporal Shared Layer Bit Adaptation (TS-LBA) for layer-wise sensitivity-based quantization. Without requiring ground-truth data, the framework uses only 100 unlabeled video clips for calibration and introduces a lightweight fine-tuning procedure guided by a full-precision BasicVSR. Experiments show that QBasicVSR significantly outperforms existing PTQ and efficient VSR methods in both accuracy and resource efficiency, achieving significant speedup and maintaining high visual quality even with 4-bit quantization.", "questions": "The reviewer notes that a 6-bit setting is used in Table 1, while later experiments adopt a 4-bit setting. This raises several questions: \n1. Is it necessary to change the calibration dataset when switching from 6-bit to 4-bit? \n2. Are the hyperparameters listed in the Experiment Section intended for the 4-bit or 6-bit setting? \n3. Additionally, the processing time for either setting is not reported, and it is unclear which bit-width is used in Table 4. These details should be clarified for consistency and reproducibility.\n\nAdditional question:\n1. What is the performance of the proposed method without calibration or fine-tuning? We suggest including this as part of the ablation study in Table 3.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. The paper proposes the first dedicated quantization method tailored for VSR, addressing a previously unexplored area. While quantization has been extensively studied for image super-resolution, its extension to video tasks poses new challenges due to temporal dependencies and recurrent structures.\n2. The paper demonstrates a successful adaptation of quantization methods to the video domain by incorporating spatiotemporal information through two novel modules: Flow-Gradient Video Bit Adaptation (FG-VBA) and Temporal Shared Layer Bit Adaptation (TS-LBA). These components are designed to dynamically allocate bit-widths based on video complexity and layer sensitivity, effectively mitigating issues such as temporal error propagation, shared temporal parameterization, and temporal metric mismatch—all of which are critical but often overlooked in existing quantization literature.\n3. The proposed framework achieves highly efficient training and inference, requiring significantly fewer computational resources compared to existing efficient VSR methods. Specifically, the method offers more than $\\times 200$ speedup and operates on just 1/8 of the GPU resources used by prior approaches. This efficiency is particularly impactful for practical applications such as real-time video enhancement on edge devices, where both speed and resource constraints are crucial.\n\n**Weaknesses:**\n1. The proposed quantization method is only applied to BasicVSR with a fixed resolution upsampling rate of $\\times 4$, which raises concerns about its generalizability to other models and scaling factors. While BasicVSR is a representative baseline, it remains unclear whether the method—or its key components—can be effectively applied to other VSR architectures. Evaluating the approach on additional models and extra upsampling scales would help demonstrate its broader applicability. \n2. The efficiency evaluation of the proposed QBasicVSR is questionable. Although the authors claim that QBasicVSR is 200 times faster and uses only 1/8 of the GPU resources compared to state-of-the-art efficient VSR methods, this comparison appears incomplete. The reported time and GPU consumption only reflect the fine-tuning phase of QBasicVSR, while the method itself starts from a pretrained BasicVSR model. The omission of the training time of BiascVSR makes the claimed efficiency gains less convincing. We also suggest including the inference time in terms of frames per second (FPS) in the main paper, as the authors mentioned real-time VSR.\n3. The calibration dataset plays a crucial role in both the calibration and fine-tuning phases. However, the main paper does not discuss how this dataset is sampled or the potential performance variability resulting from different samples; instead, this information is deferred to the supplementary material. This discussion should be included in the main text. Furthermore, the rationale for selecting 100 samples for the calibration dataset is not clearly explained.\n4. There are also several issues related to the writing of the paper. First, based on the reviewer's understanding, the Flow-Gradient Video Bit Adaptation module adjusts bit-widths at the video level for all layers uniformly, yet Line 143 implies the adjustment is performed on a per-layer basis, which is inconsistent. Second, there is an error in the latter part of Equation (6). Third, the term “FP network” appears in Line 222 without explanation; while it likely stands for “full-precision network,” this should be clearly defined in the text. We recommend that the authors clarify these points for better readability.", "was_revised": true, "final_justification": "The main novelty of the paper lies in applying quantization to video super resolution (VSR). A shared concern among reviewers was the generality of the proposed method. I appreciate the authors’ efforts to address this in the rebuttal, and the additional experiments provided have resolved my concerns. As a result, I am raising my score to 5.", "rebuttal": "**Response to Reviewer 45MJ – Weakness 1: Generalizability to Other Models and Scaling Factors**\n\n**We thank the reviewer for this suggestion.** In the video SR community, most prior works typically evaluate on **4× upscaling** rather than 2×, as the latter is relatively easier and less studied. Our focus followed this standard setting to address the more challenging 4× scenario.\n\nTo further assess generalizability, we additionally tested our method on **BasicVSRuni**, a simplified variant of BasicVSR with the backward propagation branch removed. The results showed **consistent improvements over MinMax, Percentile**, indicating that our framework generalizes well beyond the original BasicVSR model. \n\n|Methods|FQ|W/A|Process Time|REDS4 (BI)|Vimeo90K-T (BI)|Vid4 (BI)|UDM10 (BD)|Vimeo90K-T (BD)|Vid4 (BD)|\n| ---------------------- | ---- | ----- |----- | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ |\n|BasicVSR-MinMax|✓|4/4|6min|28.12 / 0.7896|34.89 / 0.9182|26.03 / 0.7585|35.37 / 0.9251|34.32 / 0.9106|25.64 / 0.7412|\n|BasicVSR-Percentile|✓|4/4|3min|27.78 / 0.7841|34.37 / 0.9159|25.23 / 0.7305|35.06 / 0.9341|34.11 / 0.9135|25.08 / 0.7275|\n|BasicVSR-MinMax+FT|✓|4/4|70min|29.21 / 0.8238|35.22 / 0.9212|26.17 / 0.7601|36.44 / 0.9393|34.88 / 0.9176|26.11 / 0.7584|\n|BasicVSR-Percentile+FT|✓|4/4|70min|28.30 / 0.8054|34.40 / 0.9159|25.26 / 0.7314|35.32 / 0.9362|34.20 / 0.9124|25.26 / 0.7340|\n|**BasicVSR-Ours**|**✓**|**4/4MP**|**90min**|**30.26 / 0.8637**|**35.82 / 0.9311**|**26.29 / 0.7752**|**37.59 / 0.9536**|**35.95 / 0.9339**|**26.81 / 0.8025**|\n|BasicVSRuni|-|32/32|-|30.54 / 0.8694|36.99 / 0.9429|27.03 / 0.8163|39.29 / 0.9646|37.27 / 0.9473|27.54 / 0.8419|\n|BasicVSRuni-MinMax|✗|4/4|4min|28.48 / 0.8012|34.40 / 0.9103|25.63 / 0.7342| 35.59 / 0.9291|34.60 / 0.9153|25.84 / 0.7550|\n|BasicVSRuni-Percentile|✗|4/4|3min|29.28 / 0.8321|35.45 / 0.9274|26.11 / 0.7717|37.19 / 0.9516|35.78 / 0.9330|26.41 / 0.7963|\n|BasicVSRuni-MinMax+FT|✗|4/4|35min|28.88 / 0.8157|34.86 / 0.9167|25.80 / 0.7441|36.34 / 0.9397|34.93 / 0.9188|26.00 / 0.7581|\n|BasicVSRuni-Percentile+FT|✗|4/4|30min|29.43 / 0.8366|35.49 / 0.9275|26.07 / 0.7681|37.32 / 0.9519|35.78 / 0.9326|26.50 / 0.7979|\n|**BasicVSRuni-Ours**|**✗**|**4/4MP**|**35min**|**29.57 / 0.8460**|**35.96 / 0.9323**|**26.32 / 0.7784**|**37.78 / 0.9550**|**36.22 / 0.9366**|**26.71 / 0.8067**|\n|BasicVSRuni-MinMax|✓|4/4|4min|28.45 / 0.7997|34.31 / 0.9088|25.56 / 0.7308|35.53 / 0.9274|34.55 / 0.9142|25.82 / 0.7531|\n|BasicVSRuni-Percentile|✓|4/4|3min|28.12 / 0.7984|33.94 / 0.9096|24.81 / 0.7030|34.76 / 0.9318|33.97 / 0.9111|24.87 / 0.7138|\n|BasicVSRuni-MinMax+FT|✓|4/4|35min|28.86 / 0.8135|34.70 / 0.9146|25.72 / 0.7384|36.33 / 0.9392|34.88 / 0.9182|26.14 / 0.7667|\n|BasicVSRuni-Percentile+FT|✓|4/4|30min|28.35 / 0.8074|34.03 / 0.9107|24.92 / 0.7105|35.05 / 0.9341|34.06 / 0.9125|25.09 / 0.7242|\n|**BasicVSRuni-Ours**|**✓**|**4/4MP**|**35min**|**29.55 / 0.8434**|**35.75 / 0.9296**|**26.15 / 0.7674**|**37.29 / 0.9517**|**35.72 / 0.9319**|**26.53 / 0.7938**|\n\n**These quantitative improvements across multiple benchmarks and models convincingly demonstrate the effectiveness of our quantization framework.**\n\n**Response to Reviewer 45MJ – Weakness 2: Efficiency Evaluation**\n\n**Thank you for your comment.** Table 4 is intended to compare **compression methods for pretrained VSR models**, not quantization versus original VSR training. For example, SSL (pruning + fine-tuning) is reported to take **8  A6000 GPUs for 370 hours**, excluding the original BasicVSR training. Likewise, our quantization-based compression takes only **1  A6000 GPU for 105 minutes**. We will clarify this scope explicitly in the revision and include the time ratio (22,200 min ÷ 105 min ≈ 211×) for transparency. And we will include the inference time in terms of frames per second (FPS) in the main paper instead of the supplementary material. We sincerely thank the reviewer again for this valuable suggestion.\n\n**Response to Reviewer 45MJ – Weakness 3: Calibration Dataset**\n\n**We thank the reviewer for this suggestion.** We agree that the role and construction of the calibration dataset should be discussed more explicitly in the main paper. **In the revised version, we will add a dedicated description of how the calibration video clips are sampled and include an analysis of the effect of calibration set size on performance in the main paper.**\n\n**To address your concern about the choice of 100 videos, we conducted an additional experiment varying the number of calibration videos and measured its impact on efficiency and reconstruction quality.** The results, as shown below, show that while performance improves as the number of calibration video clips increases, the benefit plateaus after around 100 images, whereas the processing time grows substantially. Based on this **trade-off**, we selected **100 videos** as a balanced setting for all the experiments. The evaluation results are reported on the REDS4 dataset.\n\n| num     | FQ    | W/A     | Process Time | PSNR      | SSIM       |\n| ------- | ----- | ------- | ------------ | --------- | ---------- |\n| 32      | ✓     | 4/4     | 48min        | 29.97     | 0.8512     |\n| 64      | ✓     | 4/4     | 67min        | 30.11     | 0.8565     |\n| **100** | **✓** | **4/4** | 90min       | **30.26** | **0.8637** |\n| 128     | ✓     | 4/4     | 146min       | 30.14     | 0.8582     |\n| 160     | ✓     | 4/4     | 190min       | 29.87     | 0.8512     |\n\n**Response to Reviewer 45MJ – Weakness 4: the Writing of the paper**\n\n**We thank the reviewer for these helpful comments regarding the writing.**\n\n1. Regarding the Flow-Gradient Video Bit Adaptation module: the reviewer is correct that the adjustment is applied at the video level, and in practice the bit-width assigned to a video is changed uniformly across all layers. **This is a specific manifestation of the algorithm at the video-level granularity in the current implementation.** The description in Line 143 was misleading, and we will clarify this implementation detail in the revision to avoid confusion. \n2. We also acknowledge the error in the latter part of Equation (6). **This will be corrected in the revised manuscript.**\n3. Finally, the reviewer is absolutely correct that “FP network” refers to the **full-precision network**. We will explicitly define this term at its first appearance to ensure clarity.\n\n**We sincerely appreciate the reviewer’s careful reading, which will help us improve the clarification and accuracy of the paper.**\n\n**Response to Reviewer 45MJ – Question 1: “Whether the Calibration Dataset Changes When Switching from 6-bit to 4-bit”**\n\n**We thank the reviewer for this question.**\nIn our experiments, **the calibration dataset remains fixed throughout each quantization setting**; we do not change it when switching from 6‑bit to 4‑bit. For reproducibility, we fix the random seed to 1 and sample 100 calibration video clips once for each training domain. Specifically:\n\n- From the REDS dataset, we randomly select 100 video clips (seed = 1) as the calibration set to obtain the quantized model, which is then evaluated on REDS4.\n- From Vimeo90K‑Train (BI), we sample another 100 video clips in the same way to build a quantized model, which is evaluated on Vid4 (BI) and Vimeo90K‑Test (BI).\n- From Vimeo90K‑Train (BD), we again select 100 video clips for calibration, and the resulting model is evaluated on Vid4 (BD), Vimeo90K‑Test (BD) and UDM10.\n\nThe same fixed calibration sets are used regardless of the bit-width (6‑bit or 4‑bit). Therefore, the change in bit-width does not involve re‑sampling or changing the calibration dataset.\n\n**Response to Reviewer 45MJ – Question 2: “Hyperparameters for 4‑bit vs. 6‑bit Settings”**\n\n**We thank the reviewer for this question.** The hyperparameters reported in the Experiment Section are **identical for both the 4‑bit and 6‑bit settings**. We kept all training and fine‑tuning settings fixed across bit-widths, and the consistent performance under these same hyperparameters demonstrates the robustness and generalization ability of our method.\n\n**Response to Reviewer 45MJ – Question 3: “Processing Time and Bit‑Width in Table 4”**\n\n**We thank the reviewer for this helpful suggestion.** **The processing times have been reported in the table above**. Table 4 corresponds to the **6‑bit non‑FQ quantization setting**. The previously reported 105 min in the main paper included both calibration, fine‑tuning and the evaluation of the quantized model. For fairness, we now report only the time required to obtain the quantized model, which is about 90 min.\n\n**Response to Reviewer 45MJ – Question 4: Ablation Study**\n\n**Thank you for your valuable suggestion.** We appreciate you recommending an ablation study on the performance without calibration.\n\nYour point is absolutely valid—adding this detail is necessary to help readers better understand our method. Calibration is the essential step where we determine the quantization parameters (e.g., clipping ranges). **Without the calibration stage, we cannot derive these parameters, and thus, we cannot obtain an actual quantized model.** In this scenario, the model remains and operates as the **original full-precision (FP32) model**.\n\nTherefore, as you insightfully suggested, **we will add this specific ablation point (performance of the full-precision model, i.e., without Calibration) to Table 3** in our revision. This will clearly demonstrate the baseline performance before any quantization is applied.\n\nWe also note that our original Table 3 already includes results **without Fine-Tuning (FT)**, showing the effect of quantization alone after calibration.\n\n**Once again, we are very grateful for these insightful and constructive suggestions, which have helped us strengthen the study and clarify these significant points.**"}]

Author rebuttal:

Meta-review: This paper proposes an effective temporal awareness adaptation quantization method for video super-resolution. The proposed method achieves better performance with fewer GPU resources. 

The major concerns include the generalization ability,  more comparisons with recent methods, limited novelty, and missing running time comparisons. 

Based on the provided rebuttals, the authors solve most concerns of reviewers. In addition, Reviewer zMGT still concerns about the generalization ability. The authors are suggested to address this concerns in the camera-ready version carefully.
Decision: Accept (poster)