Title: QBasicVSR: Temporal Awareness Adaptation Quantization for Video Super-Resolution
Venue: NeurIPS 2025
Accepted: yes
Keywords: video super resolution (VSR)

post training quantization (PTQ)

efficient VSR
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.

QBasicVSR: Temporal Awareness Adaptation Quantization for Video Super-Resolution

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 × 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.

1 Introduction

Super-resolution (SR) is a classic low-level computer vision task focused on reconstructing highresolution image (s) from their low-resolution (LR) counterparts. In video scenarios, it extends to aggregating spatiotemporal information from multiple misaligned frames within a sequence, requiring motion compensation and frame alignment to achieve coherent detail enhancement across the temporal domain. Despite demonstrating impressive performance, state-of-the-art video superresolution (VSR) networks [3, 4, 28, 29] face deployment challenges on resource-constrained edge 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 as reparameterization [21] and network pruning [25, 42, 46], which aim to reduce model size or simplify network structures. In parallel, model quantization has proven to be another powerful efficiency-driven 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.

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-bit 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 BasicVSR method [42] requires training 303,380 iterations with a batch size of 8, which takes approximately 15 days on 8 NVIDIA A6000 GPUs. Quantization has emerged as a critical, efficient strategy, systematically replacing 32-bit FP representations in network parameters and activation tensors with reduced-precision arithmetic, thereby achieving up to 4 × memory footprint compression and 2 -3 × latency reduction in hardwareaccelerated inference engines.

Current mainstream quantization methods can be broadly categorized into two paradigms [11]: quantization-aware training (QAT) approaches that require complete training datasets for end-toend model retraining, and post-training quantization (PTQ) methods that directly leverage pretrained models with only a few unlabeled calibration samples. While image SR quantization methods [14, 16, 26, 37, 38, 47] have achieved advanced performance with minimal degradation in accuracy, 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 architecture, which is adopted in almost all VSR models, accumulates quantization errors across interframe dependencies; (2) Shared temporal parameterization: weight-sharing in VSR across time steps introduces compounded quantization effects absent in single image processing; (3) Temporal metric mismatch: conventional image quality assessments fail to 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-aware gradient calculations. Given that VSR training already requires orders of magnitude more computation than image SR, such additional overhead makes QAT-based retraining unrealistic for practical deployment, particularly in resource-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 an 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 to reduce the heavy computation. For video streams, we propose a flow-gradient video bit adaptation strategy driven by a spatiotemporal complexity metric. Videos exceeding the upper threshold are assigned a positive factor that increases bit-width allocation. Calibration datasets are then utilized to refine 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 is calculated by processing the calibration datasets with the pre-trained FP network. Layers exhibiting sensitivity values exceeding 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 preserving temporal coherence across video frames. This dual-optimization architecture, strategically combining temporal-aware bit allocation (for both video and VSR network layers) with calibrationdriven sensitivity refinement, establishes optimized quantization scheme designs that holistically balance spatial-textural details, temporal evolution patterns, and layer-specific representation requirements throughout the video restoration pipeline. Compared to traditional VSR methods, which rely on complete labeled ground truth (GT), our method achieves rapid quantization with unprecedented 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-resolution task.

We propose a novel dual-optimization framework with a flow-gradient video bit adaptation strategy guided by spatiotemporal complexity metrics, coupled with a temporal shared layer bit adaptation module that dynamically coordinates layer-wise bit-width distribution.

Moreover, we present a 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 a substantial margin.

To promote collaborative studies and industrial deployments, we also provide QBasicVSR, a 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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2 Related Work

Model quantization has emerged as a critical technique for compressing and accelerating deep neural networks, garnering significant research attention and achieving ubiquitous adoption in modern AI systems due to its effectiveness in enabling efficient deployment across resource-constrained 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 to ultra-low 4-bit precision without catastrophic accuracy degradation by developing test-time quantization range adaptation through dynamic parameterization. Furthermore, recent works, CADyQ [14] and CABM [37], propose adaptive quantization frameworks employing content-aware bit-width prediction modules that dynamically adjust quantization levels based on input 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 prohibitively expensive in both time and computational resources.

To overcome these limitations, PTQ enables efficient model compression without retraining by calibrating 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 outliers 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 a video quantization framework that requires only a few unlabeled video clips to determine quantization parameters automatically.

3 Methodology

Our method, illustrated in Fig. 1, proposes a novel adaptive quantization method, temporal awareness 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 a flow-gradient video bit adaptation, which globally adjusts bit-widths guided by a unified spatiotemporal complexity metric. After the adaptive bit-width configurations 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-gradient video bit adaptation module.

3.1 Preliminaries

Webriefly 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-point tensor x with bit-width b , the quantization process is formulated as:

where l and u denote the lower and upper clipping thresholds, b is the quantization bit-width, and S represents the scaling factor that maps the clamped tensor x c (confined to [ l, u ] ) to 2 b discrete levels. The operator ⌊ · ⌉ implements nearest-integer rounding, while x int corresponds to the hardwarefriendly integer representation. For weights, we enforce symmetry quantizer by setting l = -u .

3.2 Flow-Gradient Video Bit Adaptation

Our goal is to determine a universal flow-gradient 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 a joint gradient-flow complexity metric that models spatial texture richness and temporal motion dynamics. Below, we formalize the components of this metric and their mathematical definitions.

Spatial Gradient Analysis: For each frame I t in a video stream of T frames, where t ∈ { 1 , . . . , T } , we compute spatial complexity by aggregating gradient magnitudes across the frame. Let Ω = { ( i, j ) | 0 ≤ i < H, 0 ≤ j < W } denote the set of all pixel coordinates in the t -th frame with height H and width W . The spatial gradient ∇ I t ( i, j ) at pixel ( i, j ) is defined as a vector containing horizontal and vertical derivatives, computed via finite differences:

The spatial complexity S t for frame t is then calculated as the mean ℓ 1 -norm of gradients across all pixels, scaled by 10 3 to amplify small magnitudes:

where ∇ I t ( i, j ) = 󰁫 ∂ I t ∂ x ( i, j ) , ∂ I t ∂ y ( i, j ) 󰁬 ⊤ , | Ω | = H × W , denotes the total number of pixels, and 󰀂 · 󰀂 1 represents the ℓ 1 -norm. 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 dynamics through bidirectional flow-field analysis. Let B t = { F f , F b } denote the set of forward ( t → t +1) and backward ( t +1 → t ) flow fields estimated by SPyNet [34], where each vector F ( i, j ) = ( u ( i, j ) , v ( i, j )) ∈ R 2 represents horizontal and vertical displacements of pixel ( i, j ) . The temporal complexity is decomposed into complementary magnitude and consistency components. The flow magnitude term quantifies average motion intensity across bidirectional flows:

where |B t | = 2 accounts for bidirectional flows, | Ω | = H × W is the pixel count, and 󰀂 · 󰀂 2 computes the ℓ 2 -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 t , we compute the Jacobian matrix J F ( i, j ) ∈ R 2 × 2 at pixel ( i, j ) , defined as:

where partial derivatives are approximated via finite differences:

and similarly for v -components. The element-wise ℓ 1 -norm 󰀂 J F ( i, j ) 󰀂 1 , 1 aggregates absolute gradients of the flow field. The consistency term aggregates the element-wise ℓ 1 -norm of Jacobians across all flows:

Finally, the temporal complexity T t combines the magnitude and consistency terms through a weighted summation:

where γ 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 C video , which unifies spatial and temporal components into a single guiding signal for bit adaptation. The overall flow-gradient complexity metric is defined as:

where S t represents the spatial complexity of the t -th frame (defined in Equation (3)), T magnitude ,t and T consistency ,t denote the temporal magnitude and consistency terms at time t (defined in Equations (4) and (7), respectively. The parameter λ balances the relative contributions of spatial texture richness and temporal motion dynamics. Finally, we design a flow-gradient driven bit-width adaptation module that maps C video to a 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 C video into a global bit-width adaptation factor b v , which dynamically adjusts the layer-wise bit-widths of the VSR network. The allocation mechanism operates as follows: if C video exceeds an upper threshold u v 2 b , the bit-width factor b v is set to +1, globally increasing the bit-widths to preserve fidelity in scenes with intricate textures or intense motion; if C video falls below a lower threshold l v 2 b , b v is set to -1, reducing bit-widths to optimize computational efficiency for simpler videos; for values within the thresholds, b v remains 0, maintaining the default quantization configuration. The thresholds l v 2 b and u v 2 b are initialized using percentile-based statistics from a small set of calibration video clips. Specifically, l v 2 b corresponds to the p V -th percentile of the video complexity distribution across calibration data, while u v 2 b is set to the (100 -p V ) -th 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.

3.3 Temporal Shared Layer Bit Adaptation

It 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 reduction without significant accuracy degradation, enabling better computational efficiency. To address the unique challenges of VSR networks, where parameters are shared across frames, we propose a temporal shared layer bit adaptation module. For each convolutional layer k , we define two complementary sensitivity metrics that jointly guide layer-wise bit-width adaptation. Given the input video sequences with T frames, we stack activations into X k stacked ∈ R T × B × C × H × W ( B : batch size, C : channels, H × W : spatial dimensions), the spatial sensitivity s k space quantifies intra-frame activation variability through channel-wise standard deviations:

where E t,b,h,w denotes averaging over time step t , batch index b and spatial positions ( h, w ) . σ c ( · ) computes the standard deviation across the channel dimension (dim=2). This captures spatial texture complexity: higher values indicate regions with edges or fine details requiring higher bit-widths. Moreover, the temporal sensitivity s k temp models inter-frame dynamics as:

where E b,c,h,w averages over batch b , channels c , and spatial coordinates ( h, w ) . σ t ( · ) calculates the unbiased standard deviation along the temporal dimension (dim=0). Elevated s k temp reflects dynamic regions with significant motion between frames. The layer-wise bit adaptation factor b k L is determined by joint thresholding:

where spatial thresholds l space and u space are initialized as the p space -th and (100 -p space ) -th percentiles of spatial sensitivity values 󰀋 s k space 󰀌 from the calibration dataset, while temporal thresholds l temp and u temp correspond to the p temp -th and (100 -p temp ) -th percentiles of temporal sensitivities 󰀋 s k temp 󰀌 . The Cartesian product [0 , l space ] × [0 , l temp ] identifies stable regions where sensitivities fall below lower percentiles, enabling bit reduction without quality degradation. Conversely, [ u space , ∞ ) × [ u temp , ∞ ) 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 layerwise factors { b k L } K k =1 , which are pre-determined and fixed during inference, leaving only the flowgradient video bit adaptation module active. After { b k L } K k =1 are determined, we further refine the activation clipping ranges using the OMSE [8] criterion. In short, the bit-width for quantizing a video stream V in layer k is dynamically adjusted through a combination of a flow-gradient video bit adaptation factor and a temporal shared layer bit adaptation factor, expressed as:

where b k V denotes the bit-width for video stream V in layer k , b base is the baseline bit-width, b v accounts for video complexity, and b k L 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

layer-wise bit adaptation factors 󰀋 b k L 󰀌 K k =1 are pre-configured and remain static during inference, we optimize them directly through gradient descent. In contrast, the video-wise bit adaptation factor b v 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 l v 2 b and u v 2 b . To overcome the non-differentiability of the thresholding operation in this module, we employ a tanh-based gradient approximation during backpropagation, inspired by the approach in [12]. Simultaneously, we optimize the clipping boundaries for activation quantization ( l a , u a ) and weight quantization ( -u w , u w ) . For the non-differentiable rounding operation in the quantizer, we utilize the Straight-Through Estimator (STE) [1], maintaining the gradient flow by approximating the rounding function as an identity operator during backward passes while preserving its exact behavior 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:

where T denotes the number of input video frames, I LR i represents the i -th low-resolution frame, P ( · ) and Q ( · ) correspond to the full-precision (FP) and quantized networks respectively, 󰀂 · 󰀂 2 is the L 2 norm measuring feature discrepancies, and 󰂃 is a small positive constant introduced to ensure numerical stability and maintain differentiability of the loss function, particularly when L pix approaches zero. For feature-level 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 L 2 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:

where T denotes the number of temporal frames, K represents the total number of feature layers, F i,k P ∈ R C k × H k × W k and F i,k Q ∈ R C k × H k × W k denote the k -th layer features for the i -th frame from P and Q respectively, with ( C k , H k , W k ) indicating the channel depth and spatial dimensions of the k -th layer outputs. The L 2 normalization ensures scale-invariant feature comparison, while the temporal summation over T frames explicitly models inter-frame relationships in video sequences. Such a supervision paradigm operates solely on low-resolution inputs, eliminating the dependency on high-resolution ground-truth references. Finally, we can get the total loss:

with λ skt 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 preserving video texture details through adaptive adjustment. The quantization parameters are iteratively optimized through three consecutive steps after the calibration phase: initially updating weight clipping ranges { u k w } K k =1 with activation and bit adaptation module parameters frozen, subsequently refining activation clipping ranges { l k a , u k a } K k =1 , and finally adjusting bit adaptive module parameters l v 2 b , u v 2 b , { b k L } K k =1 . 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], UDM10 [45], and Vimeo-90K-T [44] as test sets along with Vimeo-90K. We train and test models with 4 × downsampling using two degradations, bicubic (BI) and blur

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 γ and λ in the flow-gradient complexity metric are set to 200 and 10. The hyperparameters for calibrating the bit adaptation modules, p V , p space , and p temp are set to 10, 30 and 30. The constant 󰂃 in the reconstruction loss is set to 10 -6 . The parameter λ skt in total loss is set to 0.1. After freezing the network weights, we sequentially fine-tune 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 a batch size of 2. The initial learning rates are set to 1 × 10 -3 for weight clipping ranges, 1 × 10 -5 for activation clipping ranges, and 0 . 1 for both temporal shared layer bit adaptation and flow-gradient video bit adaptation modules. Each learning rate is decayed by 0.9 every epoch. All experiments follow the same configuration described above, and all parameters are fixed across datasets and models to ensure a 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 a 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 on REDS4 and 0.31 dB on Vimeo90K-T (BD degradation). Moreover, we retrain a unidirectional variant of BasicVSR (BasicVSR-uni) by removing its backward propagation branch, yielding a 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.

4.3 Comparison with Static Quantization

To substantiate the effectiveness of our method, we benchmark against conventional static quantization techniques without QAT. Comparisons include standard PTQ approaches MinMax [20] and Percentile [27]. To ensure fair comparison, we extend these methods by incorporating fine-tuning (FT) steps to existing PTQ approaches, denoted as MinMax+FT and Percentile+FT, where FT employs

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 a recurrent architecture with parameter sharing across frames, image-specific quantization strategies cannot be directly applied. To address this, we apply DBDC's clipping-boundary 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 (dynamic clipping with EMA) while making it compatible with recurrent video SR models, thus providing 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.

Methods

FT

W/ A

Time

BI degradation

BD degradation

(min)

REDS4 [33]

Vimeo-90K-T [44]

Vid4 [30]

UDM10 [45]

Vimeo90K-T [44]

Vid4 [30]

BasicVSR [3]

-

32 / 32

-

31.42 / 0.8909

37.18 / 0.9450

27.24 / 0.8251

39.96 / 0.9694

37.53 / 0.9498

27.96 / 0.8553

BasicVSR-MinMax [20]

✗

4 / 4

6

28.12 / 0.7896

34.89 / 0.9182

26.03 / 0.7585

35.37 / 0.9251

34.32 / 0.9106

25.64 / 0.7412

BasicVSR-Percentile [27]

✗

4 / 4

3

27.78 / 0.7841

34.37 / 0.9159

25.23 / 0.7305

35.06 / 0.9341

34.11 / 0.9135

25.08 / 0.7275

BasicVSR-DBDC [38]

✗

4 / 4

30

28.07 / 0.7817

35.23 / 0.9225

26.24 / 0.7720

35.96 / 0.9339

34.77 / 0.9172

26.01 / 0.7581

BasicVSR-MinMax+FT

✓

4 / 4

70

29.21 / 0.8238

35.22 / 0.9212

26.17 / 0.7601

36.44 / 0.9393

34.88 / 0.9176

26.11 / 0.7584

BasicVSR-Percentile+FT

✓

4 / 4

70

28.30 / 0.8054

34.40 / 0.9159

25.26 / 0.7314

35.32 / 0.9362

34.20 / 0.9142

25.26 / 0.7340

BasicVSR-DBDC+FT

✓

4 / 4

95

29.24 / 0.8232

35.45 / 0.9243

26.31 / 0.7703

36.82 / 0.9434

35.25 / 0.9228

26.42 / 0.7764

BasicVSR-Ours

✓

4 / 4MP

90

30.26 / 0.8637

35.82 / 0.9311

26.29 / 0.7752

37.59 / 0.9536

35.95 / 0.9339

26.81 / 0.8025

MinMax

24.28 / 0.6686

Percentile

24.13 / 0.6642

MinMax+FT

24.69 / 0.6883

Percentile+FT

24.23 / 0.6650

Ours

25.73 / 0.7770

GT

PSNR / SSIM

Frame 019, Clip city, Vid4

24.53 / 0.6864

DBDC+FT

25.08 / 0.7213

4.4 Ablation Study

Table 3 reports a component-wise ablation on REDS4 with 4-bit fully quantized BasicVSR. The first row corresponds to the full-precision (FP32) baseline, where no 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 on 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.

4.5 Computational Efficiency Comparison

We analyze the processing time of our framework in comparison to existing SOTA efficient methods in Table 4. In terms of processing time, our method is over × 200 faster than SSL [42]. The calculation is as follows: 370 hours × 60 = 22,200 minutes; 22,200 minutes / 90 minutes ≈ 247 × . 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 operating on the same pretrained VSR backbone. Beyond the drastic reduction in tuning cost and GPU resource demand, our method also achieves a superior runtime of 8 ms. This is notably faster than 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.

4.6 Error Bars Evaluation

PTQ for VSR requires a calibration dataset of unlabeled LR video clips to estimate activation statistics. An important consideration for practical deployment is the sensitivity of the quantized model's performance to the composition of this dataset. Notably, these calibration data can be sourced directly from the target application'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 1 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 different 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 effectively used without requiring carefully curated data. Our approach therefore provides both superior quantitative performance and practical robustness for real-world applications.

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 a 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 in 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.

Acknowledgement. This work was supported by the National Natural Science Foundation of China (Nos. 62276182, 62476196), Emerging Frontiers Cultivation Program of Tianjin University Interdisciplinary Center, Tianjin Natural Science Foundation (Nos. 24JCYBJC01230, 24JCYBJC01460), and Peng Cheng Lab Program (No. PCL2023A08).

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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. In another setting, the model is trained on the Vimeo-90K dataset [44] using two types of degradation: bicubic interpolation and a blur-downsampling approach that applies 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 UDM10 [45] is evaluated using only the blur-downsampling setting.

REDS [33] is a standard proposed high-quality video dataset with a resolution of 1280 × 720, designed for video super-resolution. It includes 270 clips that serve both training and validation purposes. Following the setting in [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 a resolution of 448 × 256, commonly adopted for video super-resolution tasks. It 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 blur-downsampling 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 a resolution of 720 × 480.

UDM10[45] is a standard benchmark dataset introduced for evaluating video super-resolution methods. It consists of 4 video clips covering diverse scenes, with each clip containing 32 frames at a resolution of 1272 × 720.

Evaluation metrics. Following [3, 33], the evaluation metrics are computed on the RGB channel for REDS4 [33], while for Vimeo-90K-T [44], Vid4 [30], and UDM10 [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 BasicVSR architecture under an FQ setting.

Stage 1: Weight Clipping Optimization. In this initial stage, we exclusively optimize the clipping parameters ( { u k w } K k =1 ) for weights, while freezing the quantization parameters ( { l k a , u k a } K k =1 ) for activations and the bit adaptation parameters ( { b k L } K k =1 , l v 2 b , u v 2 b ). The fact that this stage alone already surpasses strong baselines like DBDC [38] in accuracy, while maintaining a lower FAB, demonstrates a key finding: our framework, even with its bit adaptation modules not yet finely tuned, can achieve a superior performance-efficiency trade-off purely through optimized static quantization of weights. This establishes a strong and efficient baseline.

Stage 2: Activation Clipping Optimization. We then refine the clipping ranges ( { l k a , u k a } K k =1 ) 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 ( { b k L } K k =1 ) and the FG-VBA thresholds ( l v 2 b , u v 2 b ) . As shown in Table 6, this stage yields a significant per-

formance boost by enabling dynamic bit-width allocation, at the cost of a moderate and controlled increase in FAB. This introduces a tunable trade-off: users can opt for the high-efficiency model from Stage 1 or 2, or the high-performance model from Stage 3, depending on their specific deployment constraints. Note that the weight bit-width remains fixed at the base precision (e.g., 4 bits in "w4a4") throughout all stages; only the activation bit-width is dynamically adapted.

B.2 Full Quantization vs. Partial Quantization

In this work, we compare VSR networks with existing static quantization methods that do not utilize QAT. Meanwhile, the majority of SR quantization approaches employ a partial quantization scheme, restricting quantization exclusively to the network's body module. Therefore, we analyze the effect of fully quantizing the network in Table 7. 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 variant, BasicVSR-uni, to assess its generalizability across architectures. As shown in the lower part of the table, our method consistently outperforms existing quantization schemes on both BasicVSR and BasicVSR-uni, confirming the robustness of the proposed design.

B.3 Comprehensive Error Bars Evaluation with FAB

In our main experiments, calibration data were sampled using a fixed random seed (Seed 1) for consistency. 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) to sample calibration datasets. The results, presented in Table 8, demonstrate that our method exhibits not only stable performance (PSNR / SSIM) but also a stable 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 cannot be applied in such cases. To provide a more comprehensive 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. Compared to the efficient VSR method SSL [42], our 6-bit models (1.0-1.3M) are competitive or better in PSNR / SSIM while using far lower bits. Compared to the FP BasicVSR (32 / 32, 4.9M), our 6-bit models narrow the fidelity gap to within 0.2-0.3 dB on average while using 4-5 × fewer parameters, 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 baselines, 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 a 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 BasicVSR-uni variant. On this architecture, our quantized models maintain competitive 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

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 M1 Pro (ARM; macOS; 8 threads used). Our quantized models use 6-bit weights (64 levels) and dynamic 6-bit activation precision, executed via INT8 kernels due to current deployment constraints. Our method achieves consistent speedups across both platforms. On Intel, we observe 1.28-1.45 × speedups over BasicVSR [3] and 1.05-1.14 × over SSL [42] across all datasets.

Methods

FQ

Params(M)

GT

W/ A

REDS4 [33] (BI)

Vid4 [30] (BI)

Vid4 [30] (BD)

BasicVSR [3]

-

4.9

✓

32 / 32

31.42 / 0.8909 / 20.4533 / 42.0603

27.24 / 0.8251 / 21.2263 / 46.6820

27.96 / 0.8553 / 20.9968 / 43.2148

SSL [42]

-

1.0

✓

32 / 32

31.06 / 0.8833 / 20.5275 / 42.5137

27.15 / 0.8208 / 21.2449 / 46.4442

27.56 / 0.8431 / 20.9479 / 44.2100

Ours

✗

1.3

✗

6 / 6MP

31.26 / 0.8879 / 20.5416 / 42.6729

27.18 / 0.8215 / 21.1865 / 46.8981

27.84 / 0.8495 / 21.0283 / 44.0302

Ours

✓

1.0

✗

6 / 6MP

31.17 / 0.8849 / 20.5608 / 41.9935

27.05 / 0.8140 / 21.1727 / 45.7232

27.69 / 0.8405 / 21.0390 / 43.9466

Ours

✗

1.0

✗

4 / 4MP

30.34 / 0.8657 / 20.9095 / 46.9496

26.26 / 0.7764 / 21.0902 / 47.0901

27.02 / 0.8161 / 21.5588 / 44.8271

Ours

✓

0.7

✗

4 / 4MP

30.26 / 0.8637 / 20.7069 / 46.0018

26.29 / 0.7752 / 21.1959 / 45.3042

26.81 / 0.8025 / 21.2659 / 44.4966

MinMax [20]

✓

0.7

✗

4 / 4

28.12 / 0.7896 / 20.5620 / 42.6086

26.03 / 0.7585 / 21.7752 / 44.2720

25.64 / 0.7412 / 21.6568 / 40.8614

Percentile [27]

✓

0.7

✗

4 / 4

27.78 / 0.7841 / 20.6565 / 46.4639

25.23 / 0.7305 / 21.0079 / 48.0859

25.08 / 0.7275 / 21.3866 / 45.0816

DBDC [38]

✓

0.7

✗

4 / 4

28.07 / 0.7817 / 20.6225 / 39.5703

26.24 / 0.7720 / 21.5619 / 44.7354

26.01 / 0.7581 / 21.6323 / 41.6941

MinMax+FT

✓

0.7

✗

4 / 4

29.21 / 0.8238 / 21.1095 / 45.4638

26.17 / 0.7601 / 21.9205 / 45.7476

26.11 / 0.7584 / 22.2404 / 43.5693

Percentile+FT [27]

✓

0.7

✗

4 / 4

28.30 / 0.8054 / 20.8440 / 46.1643

25.26 / 0.7314 / 21.3187 / 48.1076

25.26 / 0.7340 / 21.5298 / 45.3654

DBDC+FT

✓

0.7

✗

4 / 4

29.24 / 0.8232 / 20.9791 / 44.9058

26.31 / 0.7703 / 21.9100 / 46.8330

26.42 / 0.7764 / 22.0107 / 43.9439

BasicVSR-uni [3]

-

2.6

✓

32 / 32

30.54 / 0.8694 / 20.6602 / 44.0094

27.03 / 0.8163 / 21.0566 / 46.3993

27.54 / 0.8419 / 20.9163 / 43.7744

Ours

✗

0.8

✗

6 / 6MP

30.40 / 0.8665 / 20.6781 / 44.1256

26.91 / 0.8101 / 21.2396 / 46.7119

27.40 / 0.8372 / 20.8535 / 44.3251

Ours

✓

0.55

✗

6 / 6MP

30.35 / 0.8646 / 20.7043 / 43.1907

26.81 / 0.8060 / 21.0356 / 46.2837

27.29 / 0.8310 / 20.8523 / 44.2879

Ours

✗

0.7

✗

4 / 4MP

29.57 / 0.8460 / 20.8091 / 45.0827

26.32 / 0.7784 / 21.7651 / 47.2370

26.71 / 0.8067 / 21.3894 / 45.7664

Ours

✓

0.4

✗

4 / 4MP

29.55 / 0.8434 / 20.7997 / 43.7369

26.15 / 0.7674 / 21.7846 / 46.7945

26.53 / 0.7938 / 21.2562 / 43.8501

MinMax [20]

✓

0.4

✗

4 / 4

28.45 / 0.7997 / 21.1814 / 45.3900

25.56 / 0.7308 / 21.2691 / 43.8372

25.82 / 0.7531 / 21.3604 / 43.3835

Percentile [27]

✓

0.4

✗

4 / 4

28.12 / 0.7984 / 20.8874 / 47.1888

24.81 / 0.7030 / 21.2692 / 47.6955

24.87 / 0.7138 / 21.2414 / 46.6296

DBDC [38]

✓

0.4

✗

4 / 4

28.36 / 0.7953 / 21.1184 / 46.4919

25.86 / 0.7510 / 21.3411 / 45.2436

25.96 / 0.7608 / 21.1965 / 43.5195

MinMax+FT [20]

✓

0.4

✗

4 / 4

28.86 / 0.8135 / 21.2081 / 44.9757

25.72 / 0.7384 / 22.2928 / 45.5211

26.14 / 0.7667 / 21.7386 / 45.0558

Percentile+FT [27]

✓

0.4

✗

4 / 4

28.35 / 0.8074 / 20.9143 / 43.3349

24.92 / 0.7105 / 21.5808 / 48.3883

25.09 / 0.7242 / 21.3034 / 46.2203

DBDC+FT [38]

✓

0.4

✗

4 / 4

28.81 / 0.8119 / 20.9934 / 46.2085

25.79 / 0.7432 / 22.4006 / 46.1070

25.98 / 0.7618 / 21.4305 / 45.4257

The ARM platform shows even more significant gains, with speedups of 1.32-5.79 × against BasicVSR [3] and 1.07-1.53 × against SSL [42]. Notably, on UDM10 [45], our ARM implementation achieves a remarkable 5.79 × speedup over BasicVSR [3] while reducing peak memory by approximately 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 unified memory architecture avoids NUMA penalties and cross-socket traffic overhead present in the dual-socket Intel system. These results demonstrate that our temporal awareness adaptation quantization approach delivers consistent acceleration and memory efficiency across diverse hardware architectures and datasets, while maintaining the reconstruction quality reported in the main paper.

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 methodology and contributions. It is important to note that AdaBM was originally designed for image SR and introduced an efficient on-the-fly 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 it a substantially different and purposebuilt solution. The core technical divergence lies in the incorporation of temporal modeling. Ad-

×

×

Algorithm 1 Temporal Awareness Adaptation Quantization for Video Super-Resolution

Input: Pretrained 32-bit VSR network P of K parameter-shared layers, calibration dataset D cal = 󰀋 V i LR 󰀌 N i =1 , spatial percentile parameter p space , temporal percentile parameter p temp . Output: Quantized VSR network Q .

1: Calibration Phase:

2: Initialize weight clipping ranges { u k w } K k =1 using OMSE [8];

3: Initialize activation clipping ranges { l k a , u k a } K k =1 with the EMA smoothed minimum and maximum statistics [20] by running calibration dataset through the FP network;

4: for i = 1 to N do

5: Calculate the video flow-gradient complexity metric C video (see Eq.\ (9));

7: Calculate the temporal sensitivity 󰀋 s k temp 󰀌 K k =1 (see Eq.\ (11)); 8: end for

6: Calculate the spatial sensitivity 󰀋 s k space 󰀌 K k =1 (see Eq.\ (10));

9: Initialize spatial thresholds l space and u space according to p space -th and (100 -p space ) -th percentiles of the spatial sensitivity values 󰀋 s k space 󰀌 K k =1 ;

11: Initialize temporal shared layer bit adaptation factor 󰀋 b k L 󰀌 K k =1 (see Eq.\ (12)); 12: Update activation clipping ranges { l k a , u k a } K k =1 using OMSE [8] depending on the temporal shared layer bit adaptation factor 󰀋 b k L 󰀌 K k =1 assigned per layer; 13: Fine-tuning Phase:

10: Initialize temporal thresholds l temp and u temp according to p temp -th and (100 -p temp ) -th percentiles of the temporal sensitivity values 󰀋 s k temp 󰀌 K k =1 ;

14: Update weight clipping ranges { u k w } K k =1 using Eq.\ (16);

16: Update bit adaptive module parameters l v 2 b , u v 2 b , { b k L } K k =1 using Eq.\ (16);

15: Update activation clipping ranges { l k a , u k a } K k =1 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 computational cost of processing video sequences compared to single images. AdaBM'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 3 epochs and demonstrates more stable optimization behavior, making it far more practical for VSR model quantization. The design of the loss function also reflects our focus on the temporal dimension. We extend beyond the spatial reconstruction loss used in image SR by introducing a 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 a bit-penalization loss to explicitly encourage lower bit-widths, we intentionally forgo such a constraint. We found that an explicit penalization could lead to optimization instability in VSR quantization. Instead, our method allows the optimal bit allocation to emerge more naturally through a data-driven optimization process, which we found to be more suitable for the complex spatio-temporal features in video data. In conclusion, while AdaBM provided valuable insights for adaptive quantization in image SR, our framework introduces critical innovations, from its temporal-aware modules and efficient training strategy to its specialized loss design and the removal of explicit bit-penalization, that collectively constitute a novel and substantially different solution tailored for the demands of VSR.

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 algorithms 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. It is better to generalize the quantized networks for other video restoration applications. Additionally, similar architectural considerations 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 experimentation in subsequent studies.

G Code

We have provided code to reproduce the results in this work.

MinMax

25.79 / 0.7310

Percentile

25.37 / 0.7136

MinMax+FT

26.62 / 0.7729

Percentile+FT

25.80 / 0.7436

Ours

27.30 / 0.8241

GT

PSNR / SSIM

Frame 077, Clip 000

Percentile+FT

28.56 / 0.8285

Ours

30.78 / 0.8838

Frame 025, Clip 011

MinMax

31.31 / 0.8578

Percentile

31.15 / 0.8695

MinMax+FT

32.54 / 0.8888

Percentile+FT

31.97 / 0.8869

Ours

33.76 / 0.9177

Frame 087, Clip 015

MinMax

28.08 / 0.8474

Percentile

27.48 / 0.8376

MinMax+FT

29.06 / 0.8741

Percentile+FT

27.94 / 0.8553

Ours

30.06 / 0.9062

Frame 062, Clip 020

GT

PSNR / SSIM

GT

PSNR / SSIM

GT

PSNR / SSIM

25.77 / 0.7334

DBDC+FT

26.69 / 0.7765

MinMax

27.98 / 0.8095

Percentile

28.17 / 0.8103

MinMax+FT

29.47 / 0.8339

28.27 / 0.8082

DBDC+FT

29.75 / 0.8493

31.31 / 0.8498

DBDC+FT

32.67 / 0.8941

28.15 / 0.8524

DBDC+FT

29.16 / 0.8784

5886

3281

3立

MinMax

33.59 / 0.9529

Percentile

33.27 / 0.9625

MinMax+FT

33.17 / 0.9623

Percentile+FT

33.65 / 0.9654

Ours

36.07 / 0.9730

GT

PSNR / SSIM

Sequence 119, Clip 005

MinMax

38.18 / 0.9591

Percentile

38.93 / 0.9694

MinMax+FT

38.54 / 0.9632

Percentile+FT

38.97 / 0.9686

Ours

41.07 / 0.9803

Frame 943, Clip 010

MinMax

35.12 / 0.9249

Percentile

35.73 / 0.9412

MinMax+FT

35.80 / 0.9364

Percentile+FT

35.79 / 0.9404

Ours

37.55 / 0.9575

Frame 087, Clip 015

MinMax

38.30 / 0.9586

Percentile

38.89 / 0.9683

MinMax+FT

39.47 / 0.9678

Percentile+FT

39.10 / 0.9688

Ours

41.11 / 0.9757

Frame 109, Clip 058

GT

PSNR / SSIM

GT

PSNR / SSIM

GT

PSNR / SSIM

34.44 / 0.9605

DBDC+FT

34.52 / 0.9659

38.37 / 0.9615

DBDC+FT

39.19 / 0.9681

35.51 / 0.9332

DBDC+FT

36.46 / 0.9442

38.85 / 0.9633

DBDC+FT

39.92 / 0.9697

OOOLOOLCOOLCOC

COOLCOOLCOOLCOOL

OIL

MinMax

21.27 / 0.7332

Percentile

20.48 / 0.7047

MinMax+FT

21.63 / 0.7455

Percentile+FT

20.48 / 0.7003

Ours

22.23 / 0.7898

GT

PSNR / SSIM

Frame 012, Clip

Calendar

MinMax

24.28 / 0.6686

Percentile

24.13 / 0.6642

MinMax+FT

24.69 / 0.6883

Percentile+FT

24.23 / 0.6650

Ours

25.73 / 0.7770

Frame 014, Clip

City

MinMax

23.46 / 0.6949

Percentile

23.18 / 0.6782

MinMax+FT

24.06 / 0.7154

Percentile+FT

23.43 / 0.6916

Ours

24.40 / 0.7451

Frame 028, Clip

Foliage

MinMax

27.44 / 0.8539

Percentile

26.48 / 0.8558

MinMax+FT

28.10 / 0.8729

Percentile+FT

26.72 / 0.8596

Ours

28.79 / 0.8937

Frame 020, Clip

Walk

GT

PSNR / SSIM

GT

PSNR / SSIM

GT

PSNR / SSIM

21.71 / 0.7559

DBDC+FT

22.05 / 0.7689

24.53 / 0.6864

DBDC+FT

25.08 / 0.7213

23.81 / 0.7140

DBDC+FT

24.10 / 0.7263

28.00 / 0.8691

DBDC+FT

28.54 / 0.8828

28

28

28

28

28

28

28

28

29

30

2

5678910

MinMax

32.79 / 0.9144

Percentile

32.08 / 0.9334

MinMax+FT

33.68 / 0.9383

Percentile+FT

32.27 / 0.9361

Ours

34.72 / 0.9570

GT

PSNR / SSIM

Frame 026, Clip

Archpeople

MinMax

28.71 / 0.8894

Percentile

27.26 / 0.8833

MinMax+FT

28.85 / 0.8998

Percentile+FT

27.37 / 0.8848

Ours

30.33 / 0.9318

Frame 007, Clip

auditorium

MinMax

41.36 / 0.9704

Percentile

42.06 / 0.9862

MinMax+FT

44.15 / 0.9849

Percentile+FT

43.18 / 0.9884

Ours

45.48 / 0.9900

Frame 015, Clip

Camera

MinMax

33.63 / 0.9377

Percentile

32.38 / 0.9450

MinMax+FT

34.85 / 0.9560

Percentile+FT

32.57 / 0.9468

Ours

36.03 / 0.9694

Frame 026, Clip

Clap

GT

PSNR / SSIM

GT

PSNR / SSIM

GT

PSNR / SSIM

33.41 / 0.9294

DBDC+FT

34.14 / 0.9424

29.25 / 0.9041

DBDC+FT

29.38 / 0.9110

42.65 / 0.9781

DBDC+FT

44.44 / 0.9854

34.46 / 0.9485

DBDC+FT

35.39 / 0.9602

535

CANO

CANO

CANO

REMOTE

CANO

CANCI

CANCI

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nThe 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nIn 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.\n\nLIMITATIONS:\nThe authors have clearly discussed the limitations of their work.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \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?\n\nQUESTIONS:\nThe experiments can be further enhanced. Please see weakness for details.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe authors have discussed relevant limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}