PMQ-VE: Progressive Multi-Frame Quantization for Video Enhancement
Authors: ZhanFeng Feng
Long Peng
Xin Di
Yong Guo
Wenbo Li
Yulun Zhang
Renjing Pei
Yang Wang
Yang Cao
Zheng-Jun Zha
Venue: NeurIPS 2025 poster ()
Keywords: quantization
deep learning
Primary area: deep_learning
TL;DR: Proposed PMQ-VE, a new quantization method for video enhancement that achieves high performance with low-bit models.

Abstract: Multi-frame video enhancement tasks aim to improve the spatial and temporal resolution and quality of video sequences by leveraging temporal information from multiple frames, which are widely used in streaming video processing, surveillance, and generation. Although numerous Transformer-based enhancement methods have achieved impressive performance, their computational and memory demands hinder deployment on edge devices. Quantization offers a practical solution by reducing the bit-width of weights and activations to improve efficiency. However, directly applying existing quantization methods to video enhancement tasks often leads to significant performance degradation and loss of fine details. This stems from two limitations: (a) inability to allocate varying representational capacity across frames, which results in suboptimal dynamic range adaptation; (b) over-reliance on full-precision teachers, which limits the learning of low-bit student models. To tackle these challenges, we propose a novel quantization method for video enhancement: Progressive Multi-Frame Quantization for Video Enhancement (PMQ-VE). This framework features a coarse-to-fine two-stage process: Backtracking-based Multi-Frame Quantization (BMFQ) and Progressive Multi-Teacher Distillation (PMTD). BMFQ utilizes a percentile-based initialization and iterative search with pruning and backtracking for robust clipping bounds. PMTD employs a progressive distillation strategy with both full-precision and multiple high-bit (INT) teachers to enhance low-bit models' capacity and quality. Extensive experiments demonstrate that our method outperforms existing approaches, achieving state-of-the-art performance across multiple tasks and benchmarks. The code will be made publicly available.

PMQ-VE: Progressive Multi-Frame Quantization for Video Enhancement
ZhanFeng Feng"t, Long Peng 11, Xin Di Yong Guo?, Wenbo Li Yulun Zhang*, Renjing Pei Yang Wang Yang Cao Zheng-Jun Zha
USTC, Max Planck Institute, $CUHK, *SITU,
Institute of Automation, Chinese Academy of Sciences, 'Chang' an University (Xiaobigfeng, longp2001, dx9826) @ maiil.utcc.edu.com
chd.edu.cn
Abstract
Multi-frame video enhancement tasks aim improve the spatial and temporal resolution and quality of video sequences by leveraging temporal information from multiple frames, which are widely used streaming video processing, surveillance, and generation. Although numerous Transformer-basee enhancement methods have achieved impressive performance, their computational and memory demands hinder deployment on edge devices. Quantization offers practical solution by reducing the bit-width of weights and activations to improve efficiency. However, directly applying existing quantization methods to video enhancement tasks often leads to significant performance degradation and loss of fine details. This stems from two limitations: (a) inability allocate varying representational capacity across frames, which results in suboptimal dynamic range adaptation; (b) over- reliance on full-precision teachers, which limits the learning of low-bit student models. To tackle these challenges, we propose a novel quantization method for video enhancement: Progressive Multi-Frame Quantization for Video Enhance- ment (PMQ-VE). This framework features a coarse-to-fine two-stage process: Backtracking-basse based Multi-Frame Quantization (BMFQ) and Progressive Multi- Teacher Distillation (PMTD). BMFQ utilizes percentile-base initialization and iterative search with pruning and backtracking for robust clipping bounds. PMTD employs a progressive distillation strategy with both full-precision and multiple high-bit bit (INT) teachers enhance low-bit models' capacity and quality. Extensive experiments demonstrate that our method outperforms existing approaches, achiev- ing state-of- oftee--f--hattttt art performance across multiple tasks and benchmarks. The code will be made publicly available.
Introduction
Multi-frame video enhancement tasks aim to enhance the spatial and temporal resolution and quality of video sequences by exploiting temporal information from multiple frames. Among these, Video Frame Interpolation (VFI) [1. 20, 25, 39, 44, 46, 50, 53, 78-80, 86. 89]. Video Super-Resolution (VSR) [3- 6, 9, 22, 26], and Temporal Video Super-Resolute Resolution (STVSR) [12, 17, 38, 66, 71, 72, 74, 87] are the most representative video enhancement methods. They are widely employed as post- post-processing and pre-pro propprccss techniques in social media platforms, gaming environments, and various video perception and generation tasks [4, 12]. Recent Transformer-bant based approaches for video enhance- ment exploit attention mechanisms to capture temporal dependencies across multiple frames, enabling substantial improvements in visual quality and structural fidelity. However, their high computational and memory demands remain major obstacle for real-world deployment. Therefore, numerous
Renjing Pei, Yang Wang and Yang Cao are the corresponding authors. ZhanFeng Feng, Long Peng, and Xin Di contributed equally to this work. Long Peng is the project leader.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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studies have proposed various model quantization methods to compress the wit-width of weights and activations from 32 bits (FP32) 8, 4, or 2 bits [19, 28, 40-42, 56, 57, 76]. This is a crucial step in practical deployment, reducing memory consumption and inference latency. For example, PAMS [28], a classic method in Post-Training Quantization (PTQ), introduces trainable scale pa- rameters to dynamically learn the maximum value of the quantization range. Liu et al. propose 2DQuant [40], a dual-stage method for image super-resolution which designs a differentiated search strategy and uses knowledge distillation [16] to guide the learning of the quantization range. However, to the best of our knowledge, exploring model quantization in video enhancement remains largely uncharted. Directly applying existing quantization methods can lead to significant issues such as performance degradation and loss of fine details. Through observations and statistical experiments, attribute these problems to two key limitations: (a) Video enhancement models need to aggregate texture and motion information from multiple frames, leading to inter-frame differential perception of information, manifesting as differentiated activation value distributions across frames shown in Figure 2()). However, traditional quantization methods fails to allocate inconsistent representational capacity different frames, resulting in discrepancies in dynamic range across frames, shown in Figure 2(b). This results in suboptimal utilization of sub-pixel spatial details, thereby limiting reconstruction performance. (b) Quantization inevitably reduces the representational capacity of the video model. Traditional methods overlook the capacity differences between the teacher (FP32) and student models (2bit, 4bit), relying solely on full- full-precision teachers for distillation. This makes challenging for the student to learn nigh-quality mappings given its limited capacity, resulting in difficulty when directly quantizing a high-pre -ieeeprciioon network into low- now-precision one, as shown in Figure (aa). To address these limitations, we propose a novel quantization framework: Progressive Multi-Frame Quantization for multi-frame Video Enhancement, called PMQ-V VE. Specifically, PMQ- VE introduces coarse-to-fin to-eine two-stage process, which includes Backtracking- based Multi-Frame Quantization (BMFQ) and Progressive Multi-Teacher Distillation (PMTD).
Coarse Stage: Backtracking- based Multi-Frama Quantization. Existing methods [40, 64] typi- cally initialize quantization bounds using the global minimum and maximum values and symmetrically shrink them inward, ignoring framr- variations and the asymmetric nature of distributions. To address this, as illustrated in Figure 2(c). BMFQ assigns trame-specife clipping bounds to better match the heterogeneous activation statistics across video frames. BMFQ employs percentile- based initialization to suppress outliers and performs a backtracking-bart based search with pruning and back- tracking to search the bounds efficiently. This strategy enables accurate, adaptive quantization with negligible overhead,
Fine Stage: Progressive Multi-Teacher Distillation. In the fine stage, introduce a Progressive Muachere Distillation framework to restore the model's representational capacity under low-bit quantization. Specifically, a full-precision teacher provides fine-grained feature supervision, while an intermediate 8/4-bit teacher offers quantization-ave guidance, helping the 4/2-bit student model learn stable and informative representations under low-bit constraints, bridging the gap between the quantized and full- precision models.
Extensive experiments on three representative video enhancement tasks STVSR, VSR, and VFI-demonstrr that our method achieves state-of-the-ar performance across multiple bench- marks for various tasks. Our method consistently outperforms existing methods, achieving the best performance on PSNR, SSIM across various bit-width settings, as shown in Figure (bb-c). The contributions of this paper can be summarized as follows:
To the best of our knowledge, we are the first to explore model quantization in multi-frame video enhancement tasks. We propose PMQ-VE. VE, novel per- fer-frame coarse- coure-to-finn to-fine quantization framework for multi-franc video enhancement models.
In the coarse stage, we propose BMFQ to search quantization bounds via iterative backtracking with pruning, achieving efficient initialization. In the fine stage, we propose PMTD to leverage the knowledge of multi-level teachers to help a low-bit student model, enhancing its mapping quality and performance.
Extensive experiments on three video enhancement tasks (STVSR, VSR, and VFI) demonstrate that our method achieves state-of-the-ar results across various benchmarks under different low-bit quantization settings, highlighting the superiority and practicability of our approach.
2

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STVSR-Vid4
STVSR-Vi44
STIR fact
STUSR Fast
VS0t 1S0--6664 Sodd
VSR 15R-3/064
MinMar
Percencle
Lensont
OAFE
Oura
2000 ard
J0CUNTT
Minfra
Vensom
OPENVINO
Zoquan:
VSR Vemo
VSK vomm
Owrs
Wi-D
VF10
(a) Visaul Comparision
(b) PSNR Comparision
(c) SSIM Comparision
Figure 1: (a) Qualitative comparison of reconstructed frames using different quantization methods.
Quantitative comparison of (SNR(b) and SSIM(c) improvements across three video enhancement tasks (STVSR, VFI, VSR). Our method consistently outperforms cxisting quantization approaches in both visual quality and quantitative metrics.
2 Related work
2.1 Video Enhancement
Video enhancement aims exploit sub-pixel information from contextual frames improve the quality and resolution of videos, which primarily includes video frame interpolation, video super- resolution, and spatio-temporal video super-resolution.
Video Frame Interpolation (VFI) targets generating the intermediate frames between given consec- utive inputs. Early CNN-based methods [1, 20, 25, 44, 89] mainly rely on optical flow estimation or direct frame synthesis, but often suffer from limited receptive fields and poor handling of large mo- tion. Therefore, Transformer- based approaches [39, 53, 86] have been proposed to model long-ran long-range dependencies, significantly improving the quality and detail of generated video.
Video Super-Resolution (VSR) aims reconstruct nigh-resolution (HR) video from lowrrssouution (LR) inputs. Early VSR methods primarily used explicit optical flow alignment [3, 5, 63], dynamic filtering [26], deformable convolutions [67], and temporal attention mechanisms [30, 73]. With the increasing prominence of the Transformer's powerful representation capabilities, numerous Transformer-based VSR methods have been proposed, achieving progressive success. For example, PSRT [62] leverages multi-fran self-attention mechanism to jointly process features from the current input frame and the propagated features. MIA [88] further boosts performance by leveraging masked intra-frame and inter-frame attention blocks to better use of previously enhanced features. Pentio-Temmpral Video Super-Resolutton (STVSR) aims to simultaneously enhance spatial and temporal resolution, combining VSR and VFI, and presents greater challenges. Among the most representative
Transformer based models RSTT [12], which achieves tfate-ofthhe-- aret performance by constructing feature dictionaries from different levels of encoders and repeatedly querying them during the decoding stage.
Although powerful transformer-based models have demonstrated superiority in enhancing spatial resolution and perceptual quality, their high computational cost hinders practical deployment. This paper is the first to propose an efficient model compression method specifically for video enhancement to facilitate its deployment.
2.2 Model Quantization
Model quantization aims to reduce the model's bit-width, from the Float 32-bit used in training to int 8, 4, or 2-bit for deployment, significantly reducing computational and memory costs and is widely applied in various fields such LLM and VLM, etc. Quantization is divided into post-training quan- tization (PTQ) and Quantization-A Aware Training (QAT). QAT, requiring simultaneous training and quantization, demands significant resources and data. PTQ. applied after pre-training, is more efficient and thus receives greater research focus. Early PTQ methods focused on minimizing quantization error using efficient calibration techniques [34, 76]. Recent approaches, such as AdaRound [70] and BRECQ [33], refine weight quantization by minimizing layer-wise output discrepancies. Additionally, robustness-oriien methods like NoisyQuant [42], OASQ [47], and ERQ [85] enhance PTQ perfor- mance by mitigating quantization noise, suppressing outliers, optimizing error-aware objectives.
3

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PSNR:30.6M
PSNR:29 BUB
Frame Prame-shored shared
0.
(b) Per-tenson Quantization
INT
0z
PSNR.26.700 78R
PSNR 23.23R
Frame (N)
7
Frame Frame-specific
Feature Value (Hima)
Bounds
Restonal
IN
Resirnal
(a) Variation Feature Distribations actoss Frames
(c) Per-frame Quantization
Residuals under different bit widths
Figure 2: Finding and Motivation. (a) multi-frame video enhancement, activation distributions vary significantly across frames. Traditional per-tensor quantization (b) fails to dynamically adjust quantization bounds for these variations, but our method (c) achieves this dynamic adjustment. (d) We calculated PSNR and residual maps for FP, INT8, INT4, and INT2 with respect to GT. The significant gap between low-bit (INT2/4) and full-precision (FP) model suggests that low-bit struggles to learn directly from FP. This inspired us multiple teacher models for supervision.
However, existing work mainly addresses high-level vision/language tasks and is often unsuitable for pixel-level image/video enhancement, which is sensitive to quantization errors due its reliance on fine-grained features. Recent research has thus started exploring quantization for pixel-level image enhancement and super-resolution [7, 8, 37, 61, 82]. For example, DBDC+Pac [64] introduces a PTQ framework for image super-resolution, combining calibration techniques with knowledge distillation from full-precce full-precision teacher model. Similarly, 2DQuant [40] targets SwinIR [35] by proposing a one-sided search algorithm to quantize sensitive activations, such as post-softmax and post-GELU [18] layers. These methods often overlook inter-frame differences in multi-frame video enhancement, limiting detail representation and resulting in blurred images.
3 Methodology
3.1 Problem Formulation
Model quantization aims to learn appropriate clipping ranges [40, 64, 76] for each tensor (e.g..
weights or activations) in order to minimize the discrepancy between the outputs of the full-precision model and the quantized model. Following previous work [40, 41, 64), we use fake quantization [24]
to simulate the quantization process. Given a pre-learned clipping range (bb, ub) for tensor x, the quantization and dequantization process is defined follows:
Telip -llamp2: (z, lb, ub) min miinmax((r; (x., lb), ub),
(1)
Teelip
lb
ub l6
Tint round
A
Fint A + lb.
(2)
2N 1
Where Telip is the clipped tensor, Tint E {0, 1,... N - 1} is the quantized integer, is the quantiza- tion step size, and è is the dequantized approximation of the original value. Linear and MatMul layers are the most computationally intensive components in Transformer-based architectures. We follow existing work [40, 42] to focus quantization on these modules. Since the quantization function is non-differentiable due to rounding, we also adopt the Strighh-Through Estimator (STE) [10] during training approximate gradients and enable end-to-end optimization under quantized settings. More details are in the Appendix A.
3.2 Observations and Motivation
Observation 1: Inability to allocate varying representational capacity across frames. Previous studies have thoroughly examined the statistical properties activations Transformer-based ar- chitectures, uncovering long-tailed distributions and mix of symmetric and asymmetric behaviors across layers [34, 40, 49, 76, 85]. However, these analyses are largely confined single-frame scenarios. In the context of quantizing multi-frame video enhancement networks, it is essential

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Freedt
BTTT
BTTB
BTBI
Stage Backtrace- based Multi-F Fulmi-Frame Quanization
Loss
Current Node (Tb ub)
Imput
FP Teacher
Quantized Model
INT Teacher
Loss sp
Prune Branches
Search Trajectory
(c) Stage Progressive Multi-Teacher Distillation
(b) BTBI
Figure 3: The overall framework of our proposed method.
to preserve the capability of the full-precision network to effectively integrate inter- frame texture information and motion cues- challenge that vanilla methods fail to address.
Our analysis reveals that multi-frame networks perceive and process each frame in the input tensor differently. As shown in Figure 2(a), we collect per-frame activation statistics and observe signif- icant disparities activation distributions across frames. In particular, the value ranges (i.e., the minimum and maximum activation values) vary considerably, indicating that the network allocates representational capacity unevenly across frames. These differences are influenced by the model's frame-dependent attention dynamics. In particular, the network assigns different attention weights each frame, resulting in varied activation distributions. Consequently, applying existing Transformer quantization methods [34, 40, 49, 76, 85], which typically assume a unified activation distribution, can be suboptimal for multi-frane models. Such methods overlook inter-frame variation, leading to inefficient quantization and increased error. ultimately degrading the quality the output.
Observation 2: Over-reliance on full- full-precision teachers limits low-bit model learning. Low-bit quantization inevitably reduces the representational capacity of models, which brings significant chal- lenges to multi-frame video enhancement tasks. Under low-bit quantization, both activation values and network precision degrade noticeably, making difficult to maintain the clear motion trajectories and rich texture details required for these tasks. As shown in Figure 2(c), directly quantizing the network to 4-bit/2-bit and applying it without adaptation leads to severe artifacts and motion blur. The main reason for this degradation lies in the large quantization errors introduced when networks directly quantized to low-bit precision. Furthermore, traditional methods often overlook the capacity gap between the teacher model (FP32) and the student model (e.g.. 4-bit or 2-bit), relying solely precision teachers for knowledge distillation [16, 40, 64]. This makes it difficult for the student model learn high-quality mappings within its limited capacity, further increasing the challenge of achieving satisfactory performance under low-bit constraints.
3.3 Proposed Method
Based on the above observations, we propose a two-stage quantization framework. The coarse stage uses Backtracking-baase Multi-Frame Quantization (BMFQ) to initialize asymmetric bounds efficiently. The fine stage applies Progressive Teach-r Distillation (PMTD) to refine bounds.
3.3.1 Backtracking-based Multi-Frame Quantization
Motivated by Observation 1, we adopt a per-frame quantization strategy to handle frame-wise variations in activation distributions and representational capacity. Given multi-frame activation tensor X E N where N denotes the number of frames, we independently search clipping bounds for each frame X; = :,), yielding frame-specific clipping parameters (lb,, ub;) for i I,....N N. This strategy enables the quantizer to adapt frame-specific activation
5

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statistics, thereby improving quantization accuracy. To robustly estimate the clipping bounds during the coarse stage, we formulate the selection of (Ib,, ub;) as a constrained optimization problem that minimizes quantization-induce distortion over a percentile-basee search space S; derived from the empirical distribution of X₃:
(162, ub?) arg min Ex x; [[x Qih.no(x))?] (Ib,ub)eS
Here, (-)) denotes a uniform quantizer [43] with clipping range [16, ubj. To mitigate the influence of out- liers, we constrain the search space S; using percentiles: lb PX)))))))) ub (X:) Po9.9(X;;)
Algorithm 1 BBacktrckkingbaase Bound
Initialization (BTBI) pipeline
Input: X, step sizes AL, AU, threshold Output: Optimal bounds 1b", _ub
where (x( denotes the k-th percentile of the ten- sor X. To efficiently solve the optimization problem in Eq. (3). we introduce Backtracking baaseeekiing--eeddddd based Bound Initialization (BTBI) algorithm. Starting from an ini- tial estimate derived from the percentiles of X,, the algorithm recursively explores candidate bounds by ad- justing (6; and ub; within the search space St. At each step, it evaluates the quantization error and updates the optimal bounds if better configuration is found. To avoid redundant searches, previously visited bounds skipped. The algorithm backtracks to explore al- ternative adjustments when no further improvement is achieved, terminating when all candidates are evaluated or a convergence threshold is met. In contrast to tra- ditional methods that uniformly shrink bounds [40] adjust them sequentially [64], BTBI is less sensitive to outliers and explores richer of candidate con- figurations. By combining Trame-wise adaptation with recursive backtracking search, BTBI robustly converges to optimal clipping parameters for each frame. To enhance understanding, we provide a detailed algorithm of our BTBI in Algorithm 1.
visited 0 errormin <- X
Function Backtrack (lb, ub):
if (Ib, ub) E visited or out-of-range
then
L return
visited + visited U {(bb ub)}
Xg + Quantize X using (lb, ub)
err + ||X||XXxlx -
if err error min + then return
if err < errormin then error f ETT lb' +b ub' f ub foreach (6,, 8u) E (+AL, =AU) do L Backtrack (lb + 5j, ub + 6.)
Backtrack(Ibp. ubp)
return 16*, .ub
3.3.2 Progressive Multi-Teacher Distillation
As revealed by Observation 2, training accurate quantized models under extremely low-bit settings (e.g., 4-bit 2-bit) remains challenging due to limited capacity and optimization instability. To address this, we propose Progressive Multi- Metti-eeacher Distillation (PMTD), a hierarchical distillation framework that leverages both high-bit and full-precision teachers facilitate low-bit training. Instead of directly distilling knowledge from a full-precision (FP) teacher to a low-bit student, which often suffers from large representational gaps, PMTD introduces intermediate-bii teacher models (c.g.. 8-bit) between the full-precision (FP) teacher and the low-bit student. These intermediate teachers serve as quantization-aware approximations of the FP model, providing smoother supervision and easing the optimization process. This hierarchical approach effectively bridges the representational gap between student and teacher models, ensuring more stable training dynamics and improved performance under extreme quantization constraints. Specifically, to train a 4-bit quantized model, PMTD first uses the full-precision model as a teacher to train an 8-bit model. When training the 4-bit model, both the full-precision network and the 8-bit network are used as teacher models, as illustrated in Figure 3(c). The distillation process is formally defined by the following loss function:
LPMID (LiNT a(l) ((1))/((+(())
where LinT denotes the total loss from intermediate-bin teachers (c.g., 8-bit), and LFP represents the loss from the full-precision teacher. The balancing coefficient a<t) linearly increases over time and defined as (t)) = min T where T Warmuu hyperparameter controlling the warm-up duration. Each teacher-specific loss consists of two components: output-level reconstruction loss

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Table 1: Quantitative comparison of different methods four STVSR benchmarks. The best and the second best results are bold and bold.
Vid4
Vimeo-Fas PSNRT
Vimco-Medium PSNRT
Vimco-Slon PSNR
Method
Bit
PSNRT
SSIMT
SSIM
SSIM
SSIM
RSTT-S [12]
32/32
26.29
0.7941
36.58
0,9381
35.43
0,9358
33,30
0.9123
Trilinear
32/32
22.90
0.5883
29.45
0.8019
29.16
0.8351
28.21
0.8091
OpenVINO [15]
2/2
18.24
0.3151
23.39
0.6103
23.60
0.6227
23.87
0.6242
TensorRT [65]
2/2
20.31
0.5118
23.41
0.6106
23.61
0.6236
23.88
0.6283
SNPE [21]
2/2
15.22
0.2378
23.40
0.6106
23.61
0.6241
23,88
0.6281
Percentile [29]
2/2
12.67
0.1349
15.27
0.2274
14.80
0,2165
14.67
0.2156
MinMax [23]
2/2
10.34
0.0138
10.52
0.0266
10.48
0.0289
10.45
0.0303
NoisyQuant [42]
2/2
12.06
0.1028
12.50
0.1669
11.81
0.1465
11.49
0.1508
DBDC+Pac [64]
2/2
22.64
0.5695
28.94
0.8254
28.87
0,8214
27.86
0.7905
2DQuant [40]
2/2
22.91
0.5883
29.38
0.8315
29.14
0.8330
28.18
0.8086
Ours
2/2
23.48
0.6252
30.33
0.8424
30.19
0,8523
29.14
0.8316
OpenVINO [15]
4/4
18.84
0.4591
21.82
0.6372
21.63
0.6315
18.84
0.4591
TensorRT [65]
4/4
18.63
0.4578
21.74
0.6019
21.70
0.6324
18.63
0.4578
SNPE [21]
4/4
17.84
0.3977
21.64
0.6018
21.56
0.6301
18.76
0.4584
Percentile [29]
4/4
23.26
0.6314
27.12
0.7664
27.16
0.7709
26.58
0.7531
MinMax [23]
4/4
21.60
0.5242
26.41
0.6990
25.94
0.7059
25.44
0.6957
NoisyQuant [42]
4/4
24.26
0.6905
31.22
0,8719
30.64
0,8705
29.61
0,8462
DBDC+Pac [64]
4/4
24.50
0.6923
32.64
0.8857
32.06
0.8866
30.64
0.8643
2DQuant [40]
4/4
25.04
0.7256
33.59
0.9035
32.83
0,9009
31.21
0.8766
Ours
4/4
25.42
0.7501
34.69
0.9181
33.74
0.9150
31.94
0.8903
and an intermediate feature-matching loss:
CINT
c//) x c((b)
(5)
CEP = Cree A Ceets
(6)
where K the number of intermediate-bit teachers (c.g., = 2 when using 4-bit and 8-bit teachers), Live is the & loss [36] between the student and teacher outputs, and Liea is the mean squared error (MSE) [2] between selected intermediate feature representations. The balancing coefficient À is set to 5 to emphasize the importance of internal consistency. By gradually transitioning supervision from intermediate-bit to full-precision teachers, PMTD effectively reduces the training difficulty of low-bit stable optimization path. This hierarchical approach ensures high-quality quantized outputs, even under extreme quantization constraints.
models, mitigates quantization errors, and offers
4 Experiment and Analysis
4.1 Experiment Setting
Datasets and backbone. We evaluate our method on three representative video enhancement tasks: Space-Time Video Super-Resolution (STVSR), Video Super-Resolution (VSR), and Video Frame Interpolation (VFI). For each task, we select state-of-the-an and popular methods backbones: RSTT [12] for STVSR, MIA [88] for VSR, and EMA-VFI [86] for VFI. The Vimeo-90K [74] dataset used for training across all tasks, with Vid4 [38] and the Vimeo-90K test set serving as evaluation benchmarks. More details of data preparation and setting are provided in the supplementary material.
Evaluation metries. We use PSNR and SSIM [68] as evaluation metrics, computed on the luminance (Y) channel of the YCbCr color space. To further evaluate perception-orrented metrics, LPIPS [81] and NIQE [48] are used to assess the perceptual quality of videos.
Implementation details. We adopt the Adam optimizer [27] with an initial learning rate of 2 x 1
7

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Table 2: Quantitative comparison of different methods two VFI EMA-VHI variants ((T) and (D)).
evaluated on the Vimeo90K benchmark under 4-bit.
EMA-VFI [T] [78]
EMA-VFI [D] [86]
Method
PSNRT
SSIMI
LPIPS|
NIQEL
PSNR
SSIM
LPIPS|
NIQEJ
Baseline
32/32
29.41
0.9279
0.086
6.736
30.29
0.9418
0.078
6.545
OpenVINO [15]
4/4
26.03
0.8703
0.222
8.022
25.38
0.8579
0.257
8.2784
TensorRT [65]
4/4
25.33
0.8551
0.268
8.582
25.21
0.8537
0.269
8,4035
SNPE [21]
25.49
0,8581
0.259
8.500
25.83
0.8683
0.236
8.0399
Percentile [29]
4/4
26.82
0.8919
0.185
7.765
28.54
0.9198
0.132
7.0667
MinMax [2.3]
4/4
23.03
0.7918
0.389
9.475
24.19
0.8309
0.313
8,5153
DBDC+Pac[64]
4/4
27.30
0.8976
0.171
7.545
28.36
0.9179
0.134
7.1221
2DQuant [40]
4/4
28.06
0.9110
0.152
7.494
28.78
0.9233
0.120
6.9884
Ours
4/4
28.41
0,9162
0.136
7.361
29.59
0.9335
0.101
6.7881
Table 3: Quantitative comparison of different methods on two VSR benchmarks under 4-bit.
Baseline: MIA [88]
TensorRT
SNPE
Percentile
MinMax [23]
DBDC +Pac [64]
2DQuant
Benchmark
Metric
Ours
[65]
[21]
[29]
[40]
PSNRT
38.32
31.81
32.42
35.15
34.13
36.64
36.92
37.34
Vimeo90K
SSIM
0.9532
0.8612
0.8805
0.9262
0.9119
0.9404
0.9434
0.9487
PSNRT
28.20
24.48
24.22
26.14
25.60
27.26
27.38
27.64
Vid4
SSIM
0.8507
0.6758
0.6877
0.7805
0.7494
0.8230
0.8287
0.8341
and apply Cosine Annealing [45] over 20,000 iterations. The batch size is set to 8 and 2 per GPU during the initialization and distillation-based fine-tuning phases, respectively. Random cropping. rotations, and flipping are applied to enhance training robustness. All experiments are implemented in Python with PyTorch [54] and conducted on 8 NVIDIA V100 GPUs.
4.2 Quantitative Results
Table presents quantitative comparisons of various methods under 2/2, 4/4, bit-width across four STVSR benchmarks. Traditional quantization approaches, such as Open VINO [15] and Ten- sorRT [65], face challenges in pixel-level video enhancement, resulting in model performance scores of only 18.24dB and 20 Vid4 at 2-bit quantization. Although DBDC+Pac [64] and 2DQuant [40] are tailored for low-level vision tasks with enhanced sharpness awareness, which somewhat mitigates the performance drop due to quantization, they still lag behind proposed method. This is primarily due to their limitations in managing multi-frame distribution differences and detail enhancement. Our method achieves the best performance across all scenarios and benchmarks, notably surpassing existing methods by nearly 1 dB on the Vimeo benchmark, underscoring the effectiveness of our approach. In similar manner, across all benchmarks and bit-width configurations for both Video Super-Resolution (VSR) and Video Frame Interpolation (VFI) tasks. As detailed in Tables 2 and 3, our approach exemplifies remarkable generalization capabilities, consistently outperforming existing methods all benchmarks and bits. More results on additional bit-width settings and benchmarks be found in Appendix E.
method demonstrates superior performance
4.3 Qualitative Results
To verify the visual quality, we present the visual comparisons of different PTQ methods applied to STVSR, VSR and VFI tasks under 4-bit quantization, as shown Figure Traditional methods such as MinMax [23] and Percentile [29] exhibit noticeable artifacts, while other methods like DBDC+Pac [64] and 2dquant [40] suffer from detail blurring issues, particularly in complex scenes. However, our proposed method consistently produces sharper edges and more faithful textures that are visually closer the full-preet precision outputs. In more challenging cases, such as the Vimeo-Fase dataset where motion and fine details coexist, proposed method better preserves structural information
8

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GT
Tensont
MinMax
Paronnile
DBDC
2dkquant
Ours
FP
GT
Tensont
MinMax
Pergenble
DBDCHPac
2dquant
Ours
FP
Tonsont
MinMax
Parozntlee
DPDC
2dquuant
Ours
FP
Figure 4: Visual comparisons under b-t quantization for three video enhancement tasks: from top to bottom are STVSR, VSR, and VFI tasks. More results are provided in Appendix F.
and avoids common artifacts. This highlights the visual superiority of our approach. More visual effects and comparisons with studies will be presented in the Appendix F.
4.4 Ablation Studies
To validate the effectiveness of the proposed core idea, design several ablation experi- ments to explore the multi-teacher distillation strategy the frame-wise quantization strategy. STVSR in 2-
Specifically, conduct experiments bit compression setting, removing these core modules one by one, with results shown in Table It is seen that the baseline without any core ideas achieves only 12.67dB. By introducing the frame-wise quantization strategy, the network perceives differences between frames, improving performance. Furthermore, the BMFQ helps the network adaptively learn clipping ranges for each frame, boosting
Table 4: Ablation studies.
Per-Frame Quantization BMFQ PMTD
PSNRT
x
X
12.67
X
X
19.64
X
27.56
30,33
model performance to 27.56dB Finally, with the introduction of the multi- distillation strat- egy, the low-bit network learns prior knowledge from different teachers, further improving model performance despite limited capacity. This validates the effectiveness of the proposed core modules. More ablation studies are presented in the Appendix H.
Conclusion
We introduced novel coarse-to-fine fine PMQ-VE, addressing key challenges in quantizing multi-frame video enhancement models. BMFQ is proposed establish robust quantization bounds through percentile-baze based initialization and backtracking search, ensuring efficient quantization across frames. PMTD enhances the quality low-bit models by utilizing a progressive distillation strategy with both full-precision and quantized teachers, bridging the gap between high-precision and low-bit models. Experiments on STVSR, VSR, and VFI tasks show that our PMQ-VE achieves state-of-the-art performance and visually pleasing results.

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Limitation and Future Work: Although our PMQ-VE has achieved promising results on Transformer- based video enhancement methods, more diffusion-basee Transformer (DiT) methods can be tested. In the future, we plan to extend our method to more video enhancement tasks and models to facilitate the deployment of video models in the community.
10

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Acknowledgments
This work was supported by the Natural Science Foundation of China under Grants 62225207, 62225207,62436008 62436008 and 62206262. The AI-driven experiments, simulations and model training were performed on the robotic Al-Scientist platform of Chinese Academy of Sciences.
References
[1] Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Hsuan Yang. Depth-aware video frame interpolation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3703-3712, 2019.
[2] Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning, 36:105-139, 1999.
[3] Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, and Wenzhe Shi. Real-time video super-resolution with spatio-temporal networks and motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4778-4787, 2017.
[4] Jiezhang Cao, Yawei Li, Kai Zhang, and Luc Van Gool. Video super-resolution transformer. arXiv preprini arXiv:21066006477 2021.
[5] Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basicvsr: The
search for essential components in video super-resolution and beyond. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4947-4956, 2021.
[6] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improv- ing video super-resolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5972-5981, 2022.
[7] Yujie Chen, Haotong Qin, Zhang Zhang, Michelo Magno, Luca Benini, and Yawei Li. Q-mambair: Accurate quantized mamba for efficient image restoration. arXiv preprint arX11 2025.
[8] Zheng Chen, Haotong Qin, Yong Guo, Xiongfei Su, Xin Yuan, Linghe Kong, and Yulun Zhang, Binarized diffusion model for image super-resolution. arXiv preprint arXiv:2406.05723 2024.
[9] Myungsub Choi, Heewon Kim, Bohyung Han, Ning Xu, and Kyoung Mu Lee. Channel attention is all you need for video frame interpolation. In Proceedings of the AAAI conference artificial intelligence, volume 34, pages 10663-10671, 2020.
[10] Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran EI- Ei-Yaniv, and Yoshua Bengio. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ I or-1. arXiv preprint 160.:::662000283300 2016.
[11] Xin Di, Long Peng, Peizhe Xia, Wenbo Li, Renjing Pei, Yang Cao, Yang Wang, and Zheng- Jun Zha. Qmambabsr: Burst image super-resolution with query state space model. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 23080-200 230900, 2025.
[12] Zhicheng Geng, Luming Liang, Tianyu Ding, and Ilya Zharkov. Rstt: Real-time spatial temporal transformer for space-time time video super-resolution In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 17441-17451, 2022.
[13] Yunpeng Gong, Liqing Huang, and Lifei Chen. Person re-identification method based on color attack and joint defence. In CVPR, 2022, pages 4313-4322, 2022.
[14] Yunpeng Gong, Chuangliang Zhang, Yongjie Hou, Lifei Chen, and Min Jiang. Beyond dropout: Robust convolutional neural networks based on local feature masking. 2024 International Joint Conference Neural Networks (LJCNN), pages 1-8. IEEE, 2024.
11

---

[15] Yury Gorbachev, Mikhail Fedorov, llya Slavutin, Artyom Tugarev, Marat Fatekhov, and Yaroslav Tarkan. Openvino deep learning workbench: Comprehensive analysis and tuning of neural networks inference. In Proceedings of the IEEF/CVF International Conference on Computer Vision Workshops, pages 0-0, 2019.
[16] Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. Knowledge distillation: A survey. International Journal of Computer Vision, 129(6):189--1899 2021.
[17] Muhammad Haris, Greg Shakhnarovich, and Norimichi Ukita. Space-time-aware multi- resolution video enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2859-2868, 2020.
[18] Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016.
[19] Cheeun Hong, Heewon Kim, Sungyong Baik, Junghun Oh, and Kyoung Mu Lee. Daq: Channel- wise distribution-wware quantization for deep image super-resolution networks. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2675-2684, 2022.
[20] Zhewei Huang. Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. Real-time inter- mediate flow estimation for video frame interpolation. In European Conference on Computer Vision, pages 624-642. Springer, 2022.
[21] Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, and Luc Van Gool. Ai benchmark: Running deep neural networks on android smartphones. In Pro- ceedings of the European Conference Computer Vision (ECCV) Workshops, pages 0-0, 2018.
[22] Takashi Isobe, Xu Jia, Shuhang Gu, Songjiang Li, Shengjin Wang, and Qi Tian. Video super- resolution with recurrent structure-detar network. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XII 16, pages 645-660. Springer, 2020.
[23] Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient
inference. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2704-2713, 2018
[24] Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer- ariggmerrithmmtii-nnnyy inference. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages
2713, 2018.
[25] Zhaoyang Jia, Yan Lu, and Houqiang Li. Neighbor correspondence matching for flow-based video frame synthesis. In Proceedings of the 30th ACM International Conference on Multimedia, pages 1389-5397, 2022.
[26] Younghyun Jo. Scoung Wug Oh, Jaeyeon Kang, and Scon Joo Kim. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition. pages 3224-32223 2224-3232, 2018.
[27] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[28] Huixia Li, Chenqian Yan, Shaohui Lin, Xiawu Zheng, Baochang Zhang, Fan Yang, and Ron- grong Ji. Pams: Quantized super-resolution via parameterized max scale. In Computer Vision- ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28. 2020, Proceedings, Part XXV 16, pages 564-580. Springer, 2020.
[29] Rundong Li, Yan Wang, Feng Liang, Hongwei Qin, Junjie Yan, and Rui Fan. Fully quantized network for object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2810-2819,
12

---

[30] Wenbo Li, Xin Tao, Taian Guo, Lu Qi. Jiangbo Lu, and Jiaya Jia. Mucan: Multi-correspoodence aggregation network for video super-resolution. In Computer Vision-ECo ECCV 2020: 16th Euro- pean Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part X 16, pages 335-351. Springer, 2020.
[31] Wenjie Li, Juncheng Li, Guangwei Gao, Weihong Deng, Jiantao Zhou, Jian Yang, and Guo-Jun Qi. Cross-receptive focused inference network for lightweight image super-resolution. IEEE Trunsuctions on Multimedia, 26:864 877, 2023.
[32] Wenjie Li, Heng Guo, Xuannan Liu, Kongming Liang, Jiani Hu, Zhanyu Ma, and Jun Guo. Efficient face super-resolution via wavelet-based feature enhancement network. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 4515-4523, 2024.
[33] Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. Breeq: Pushing the limit of post-training quantization by block reconstruction. arXiv preprint arXiv:2102.056
2021.
[34] Zhikai Li, Junrui Xiao, Lianwei Yang, and Qingyi Gu. Repq-vit: Scale reparameterization for post-training quantization of vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 17227-17236, 2023.
[35] Jingyun Liang. Jiezhang Cao, Guolei Sun, Kai Zhang, Lue Van Gool, and Radu Timofte. Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1833-1844, 2021.
[36] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super-resolute ruseutrcsolu In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 136-144, 2017.
[37] Boyu Liu, Haoyu Huang. Linlin Yang, Yanjing Li, Guodong Guo, Xianbin Cao, and Baochang Zhang. Efficient low-bit quantization with adaptive scales for multi-task co-training. In The Thirteenth International Conference on Learning Representations.
[38] Ce Liu and Deqing Sun. On bayesian adaptive video super resolution. IEEE transactions on pattern analysis and machine intelligence, 36(2):346-360, 2013.
[39] Chunxu L Liu, Guozhen Zhang, Rui Zhao, and Limin Wang. Sparse global matching for video frame interpolation with large motion. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pages 19125-19134, 2024.
[40] Kai Liu, Haotong Qin, Yong Guo, Xin Yuan, Linghe Kong, Guihai Chen, and Yulun Zhang. 2dquant: Low-bit post-training quantization for image super-resolution. Advances in Neural Information Processing Systems, 37:7 11771068--108.. 2024.
[41] Kai Liu, Dehui Wang. Zhiteng Li, Zheng Chen, Yong Guo, Wenbo Li, Linghe Kong, and Yulun Zhang. Condiquant: Condition number based low-bit quantization for image super-resolution. arXiv preprint arXiv:2502.15478 2025.
[42] Yijiang Liu, Huanrui Yang, Zhen Dong, Kurt Keutzer, Li Du, and Shanghang Zhang. Noisyquant: Noisy bias-enhanced post-training activation quantization for vision transformers. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20321-20330, 2023.
[43] Zechun Liu, Kwang- Kwang-Ting Cheng, Dong Huang, Eric P Xing, and Zhiqiang Shen. Nonuniform-to uniform quantization: Towards accurate quantization via generalized ttrought estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4942-4952, 2022.
[44] Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala. Video frame synthesis using deep voxel flow. In Proceedings of the IEEE international conference on computer vision, pages 4463-4471, 2017.
[45] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint 1608.066600009900 2016.
13

---

[46] Liying Lu, Ruizheng Wu, Huaijia Lin. Jiangbo Lu, and Jiaya Jia. Video frame interpolation with transformer. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3532-3544 2022.
[47] Yuexiao Ma, Huixia Li, Xiawu Zheng, Feng Ling, Xuefeng Xiao, Rui Wang, Shilei Wen, Fei Chao, and Rongrong Ji. Outlier-aware slicing for post-training quantization in vision transformer. In Forty-first International Conference on Machine Learning, 2024.
[48] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a "completely blind" image quality analyzer. IEEE Signal processing letters, 20(3):209- 212, 2012.
[49] Jachyeon Moon, Dohyung Kim, Junyong Cheon, and Bumsub Ham. Instance-aware group quantization for vision transformers. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages (6132-16141) 2024.
[50] Simon Niklaus and Feng Liu. Context-aware synthesis for video frame interpolation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701- 1710, 2018.
[51] Jiancheng Pan, Qing Ma, and Cong Bai. Reducing semantic confusion: Scene-aware aggregation network for remote sensing cross-modal retrieval. In Proceedings of the 2023 ACM International Conference on Multimedia Retrieval, pages 398-406, 2023.
[52] Jiancheng Pan, Yanxing Liu, Xiao He, Long Peng, Jiahao Li, Yuze Sun, and Xiaomeng Huang. Enhance then search: An augmentation-seat strategy with foundation models for cross-domain few-shot object detection. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 1548-1556 2025.
[53] Junheum Park, Jintae Kim, and Chang- Su Kim. Biformer: Learning bilateral motion estimation via bilateral transformer for 4k video frame interpolation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition. pages 1568-1577, 2023.
[54] A Paszke. Pytorch: An imperative style, tigh-performance deep learning library. arXiv preprint 1rXiv:191..0103,, 2019.
[55] Long Peng, Yang Cao, Yuejin Sun, and Yang Wang. Lightweight adaptive feature de-drifting for compressed image classification. IEEE Transactions on Multimedia, 26:6424-6436 2024.
[56] Long Peng. Wenbo Li, Jiaming Guo, Xin Di, Haoze Sun, Yong Li, Renjing Pei, Yang Wang. Yang Cao, and Zheng-Jun Zha. Unveiling hidden details: A raw data-enhanced paradigm for real-world super-resolution. arXiv preprini arXiv:24/1 1. 10798, 2024.
[57] Long Peng, Wenbo Li, Renjing Pei, Jingjing Ren, Jiaqi Xu, Yang Wang. Yang Cao, and Zheng- Jun Zha. Towards realistic data generation for real-world super-resolution. arXiv preprint arXiv:2406.07255, 2024.
[58] Long Peng, Xin Di, Zhanfeng Feng, Wenbo Li, Renjing Pei, Yang Wang, Xueyang Fu, Yang Cao, and Zun Zha. Directing mamba to complex textures: An efficient texture-aware state space model for image restoration. arXiv preprint arXiv:2501. 2025.
[59] Long Peng. Yang Wang, Xin Di, Xueyang Fu, Yang Cao, Zueng-Jun Zha, et al. Boosting image de-raining via central- surrounding synergistic convolution. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 6470-6476 6478, 2025.
[60] Long Peng, Anran Wu, Wenbo Li, Peizhe Xia, Xueyuan Dai, Xinjie Zhang, Xin Di, Haoze Sun, Renjing Pei, Yang Wang, et al. Pixel to gaussian: Ultra-fast continuous super-resolution with 2d gaussian modeling. arXiv preprini arXiv:2503.0666 2025.
[61] Haotong Qin, Yulun Zhang, Yifu Ding, Xianglong Liu, Martin Danelljan, Fisher Yu, al. Quantsr: accurate low-bit quantization for efficient image super-resolution. Advances in Neural Information Processing Systems, 11555888-56444 2023.
[62] Shuwei Shi, Jinjin Gu, Liangbin Xie, Xintao Wang, Yujuu Yang, and Chao Dong. Rethinking alignment in video super-resolution transformers. Advances Neural Information Processing Systems, 35:36081-36993, 2022.
14

---

[63] Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, and Jiaya Jia. Detail Detail-revealing deep video super-resolution. In Proceedings of the IEEE international conference on computer vision, pages
2017.
[64] Zhijum Tu, Jie Hu, Hanting Chen, and Yunhe Wang. Toward accurate post-training quantization for image super resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5856-5865, 2023.
[65] Han Vanholder. Efficient inference with tensortt. In GPU Technology Conference, volume 1, pages 1-24, 2016.
[66] Hai Wang, Xiaoyu Xiang, Yapeng Tian, Wenming Yang, and Qingmin Liao. Stdan: deformable attention network for space-time video super-resolution. IEEE Transactions on Neural Networks and Learning Systems, 2023.
[67] Xintao Wang. Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. Edvr: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition workshops, pages 0-0, 2019.
[68] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4): 600-612, 2004.
[69] Pingyu Wu, Kai Zhu, Yu Liu, Liming Zhao, Wei Zhai, Yang Cao, and Zue Zha. Improved video vae for latent video diffusion model. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 18124-18133 2025.
[70] Zhuguanyu Wu, Jiaxin Chen, Hanwen Zhong, Di Huang, and Yunhong Wang. Adalog: Post- training quantization for vision transformers with adaptive logarithm quantizer. In European Conference on Computer Vision, pages 41 1-427. Springer, 2024.
[71] Xiaoyu Xiang, Yapeng Tian, Yulun Zhang, Yun Fu, Jan P Allebach, and Chenliang Xu. Zooming slow-mo: Fast and accurate one-stage space-time video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3370-3379, 2020.
[72] Gang Xu, Jun Xu, Zhen Li, Liang Wang, Xing Sun, and Ming-Ming Cheng. Temporal modulation network for controllable space-time video super-resolution. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 6388-6397, 2021.
[73] Kai Xu, Ziwei Yu, Xin Wang. Michael Bi Mi, and Angela Yao. Enhancing video super-resolution via implicit resampling-base alignment. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 2546-2555 2024.
[74] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman. Video enhancement with task-oriented flow. International Journal of Computer Vision, 127: 177:1106-112: 1125, 2019.
[75] Jian Yang, Dacheng Yin, Yizhou Zhou, Fengyun Rao, Wei Zhai, Yang Cao, and Zheng- Zha. Mmar: Towards lossless multi modal auto-regressive probabilistic modeling. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 7974-7985, 2025.
[76] Zhihang Yuan, Chenhao Xue, Yiqi Chen, Qiang Wu, and Guangyu Sun. Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization. In European conference on computer vision, pages 191-207. Springer, 2022.
[77] Wei Zhai, Yang Cao, Jing Zhang, Haiyong Xie, Dacheng Tao, and Zheng-Jun Zha. On exploring multiplicity of primitives and attributes for texture recognition the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1)::03-420) 2023.
[78] Guozhen Zhang, Yuhan Zhu, Haonan Wang, Youxin Chen, Gangshan Wu, and Limin Wang. Extracting motion and appearance via inter-frame attention for efficient video frame interpola- tion. Proceedings of the IEFF/CVE Conference on Computer Vision and Pattern Recognition, pages 5682-5692 2023. 2023.
15

---

[79] Guozhen Zhang, Yuhan Zhu, Haonan Wang, Youxin Chen, Gangshan Wu, and Limin Wang. Extracting motion and appearance via inter-frame attention for efficient video frame interpola- tion. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 5682-5692, 2023.
[80] Guozhen Zhang, Chuxnu Liu, Yutao Cui, Xiaotong Zhao, Kai Ma, and Limin Wang. Vfimamba: Video frame interpolation with state space models. Advances in Neural Information Processing Systems, 37:107225-107248 2024.
[81] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unrea- sonable effectiveness of deep features perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018.
[82] Yulun Zhang, Haotong Qin, Zixiang Zhao, Xianglong Liu, Martin Danelljan, and Fisher Yu. Flexible residual binarization for image super-resolution. In Forty-first International Conference on Machine Learning, 2024.
[83] Yaozong Zheng, Bineng Zhong, Qihua Liang, Ning Li, and Shuxiang Song, Decoupled spatio-temporal consistency learning for self-supervised tracking. In Proceedings of the AAAI Conference Artificial Intelligence, volume 39, pages 10635-10643, 2025.
[84] Yaozong Zheng. Bineng Zhong, Qihua Liang, Shengping Zhang, Guorong Li, Xianxian Li, and Rongrong Ji. Towards universal modal tracking with online dense temporal token learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
[85] Yunshan Zhong, Jiawei Hu, You Huang, Yuxin Zhang, and Rongrong Ji. Erq: Error reduction for post-training quantization of vision transformers. In Forty-first International Conference on Machine Learning, 2024.
[86] Zhihang Zhong, Gurunandan Krishnan, Xiao Sun, Yu Qiao, Sizhuo Ma, and Jian Wang. Clearer frames, anytime: Resolving velocity ambiguity in video frame interpolation. In European Conference on Computer Vision, pages 346-363. Springer, 2024.
[87] Chengeheng Zhou, Zongqing Lu, Linge Li, Qiangyu Yan, and Jing-Hao Xue. How video super-resolution and frame interpolation mutually benefit. In Proceedings of the 29th ACM International Conference on Multimedia, pages 5445-5453, 2021.
[88] Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, and Shuhang Gu. Video super-resolution transformer with masked inter&intra-frame attention. In Proceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages
2024.
[89] Yuhan Zhu, Guozhen Zhang, Jing Tan, Gangshan Wu, and Limin Wang. Dual detrs for multi- label temporal action detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18569,18569, 2024.
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Appendix / Supplemental material
A More Details about Quantization Scheme
This section provides further elaboration on the quantization process introduced in the problem formulation. We detail the quantization strategy employed for Transformer based architectures and describe how gradient approximation is performed using the Straight-Throup Estimator (STE), enabling end-to-end training under quantized constraints.
A.1 Quantization Strategy for Transformer Blocks
Following prior work [40, 42], we apply quantization to the most computationally intensive components of Transformer models, including all linear transformations and batched matrix multiplications. To simulate quantization during training, we adopt fake quantization approach, wherein both weights and activations are quantized using floating-poim operations that emulate integer arithmetic. Figure 5 illustrates the quantization workflow within a typical Transformer block, covering the QK attention mechanism, Multi-Head Self-Attention (MSA), and the feed-forward network (MLP). All Q-Linear layers operate quantized weights and activations [13, 14, 69, 75, 77, 83, 84], which are processed through dedicated quantizers prior to arithmetic operations. For simplicity, dropout layers in the attention and projection modules are omitted in the illustration.
Quantized Layer
Q-Linear
Q-Linear
Q-Linear
FP32 Layer/Tensor
LayerNorm
Fake Quantizer
K
Act Quantizer
MSA
Quantizer
Weight
Matrix Matmul
Weight Quantizer
LayenNorm
Quantizer
Softmax
Q-Linear
Matrix Matmul
Matrix Matmul
MLP
GELU
Q-Linear
Q- Linear
Figure 5: Quantization scheme for Transformer blocks. Green boxes denote quantized layers, yellow boxes indicate FP32 operations and tensors, and blue boxes represent fake quantizers. Q-Linear indicates quantized linear transformation. Each matrix multiplication is performed on quantized weights and activations.
A.2 Gradient Approximation with STE
However, both the Clip and Round operations are non-differentiable which impedes the use of gradient-based optimization methods [31, 32, 51, 52]. To overcome this, we adopt the Straight- Through Estimator (STE), where the gradients are approximated during backpropagation.
Specifically, for the Round operation, the gradient is approximated
Round((r) dx
1.
(7)
For the Clip operation, the partial derivatives are approximated as:
aClip(æ, Or 0. otherwise,
[1, I x S Su,
(8)
8Clip(æ, 1, f1, if << aClip(æ,1,,)
11 if <> u 01 0, otherwise, Ou otherwise.

---

By leveraging these approximations, PTQ frameworks can fine-tune the clipping parameters (lu } for weights and {la. un) for activations, enabling better adaptation low-bit constraints without altering the original floating-point model parameters.
A.3 Distinguishing Our PTQ Approach from QAT
To prevent potential ambiguity, explicitly define our methodology Post-Training Quantiza- tion (PTQ). A core principle of our approach is that throughout the entire quantization process, the weights of the pre-trained full-precision model are kept completely frozen and are never updated [11, 55, 57-60J. The optimization is exclusively aimed at determining the optimal quantization parameters, specifically the clipping bounds for both weights and activations.
A key aspect of our method is the use of fake quantization and the Straight- Through Estimator (STE) during the optimization of these clipping bounds. It is crucial to understand that these techniques are employed solely as a mechanism to enable gradient-based optimization of the clipping bounds through backpropagation. This strategy of optimizing quantization parameters while keeping weights fixed is a common and advanced technique in the PTQ literature, enabling more accurate parameter learning without the significant computational overhead of full model retraining [40, 76].
A.4
Clarification on Multi-Frame Quantization
We wish to clarify the term "multi-frame quantization" to accurately reflect its implementation and scope. The term does not imply that we store unique set of quantization parameters for every single frame of an entire video sequence, Such an approach would be impractical, leading to prohibitive storage costs and failing to generalize to videos of arbitrary length.
Instead, our definition of multi-frame quantization is tailored the operational architecture of modern video enhancement models. These models, whether based on structures, process a fixed number (N) of frames each inference step. For instance, a sliding- window model takes a local window of N frames as input to produce an enhanced central frame. Consequently, our "per-frame quantization" refers to the practice of learning and storing N distinct sets of clipping bounds-or one for each of the N frame slots within this fixed-size processing window. This fine- men-grained approach captures local temporal dynamics more effectively, leading to superior quantization performance.
window or recurrent
More Details of Datasets and Training Settings.
In this work, we use the Vimeo-90K septuplet dataset [74] as the primary training dataset across all three tasks. The Vimeo-90K dataset contains 64,612 training clips and 7,824 testing clips, with each clip consisting of seven frames at a spatial resolution of 448 x 256. For evaluation, in addition to the Vimeo-90K test set, we also adopt the Vid4 dataset [38], a classical benchmark containing four video sequences (calendar, city, foliage, and walk). During the PTQ training phase, adopt a knowledge distillation strategy where the supervision relies on teacher model predictions rather than HR references. However, the usage of the Vimeo-90K dataset slightly varies across tasks due to the differences in task requirements and experimental protocols, as detailed below.
For the STVSR task, we the Vimeo-90K dataset [74] for training. each video clip in Vimeo-90K is treated as high-resolution (HR) and high-frame-rai rate (HFR) references. The low-resolution (LR) and low-frame-rate (LFR) inputs are generated by selecting odd-index frames from the HR sequences and downsampling them by a factor of 4 using bicubic interpolation. Following standard practices in previous STVSR studies [12, 66, 72], we evaluate the model on both the Vid4 dataset and the Vimeo-90K test set. The Vimeo-90K test set is further divided into Vimeo-Slow. Vimeo-Medium, and Vimeo-Fast subsets based on motion magnitude. To avoid infinite PSNR values, we exclude six sequences from the Vimeo-Medium subset and three from the Vimco-Slow subset, as suggested in [66].
For the VSR task, we follow the experimental settings of prior works [5, 6, 62]. The Vimeo 9 dataset is used for training. with its seven-frame clips serving as training samples. For evaluation, we adopt both the Vimeo-90K test set (Vimeo90K-1) T) and the Vid4 dataset to benchmark model performance.
For the VFI task, we train our models on the Vimeo-90K septuplet dataset by following the experimental settings introduced in [88]. Two training paradigms are employed: (1) the setting, which follows the traditional arbitrary time indexing paradigm where temporal interpolation is
25

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performed at uniformly sampled time steps, and (2) the D setting, which adopts distance- based indexing strategy, prioritizing frames closer to the input during interpolation. line with [88], we use the first and last frames of each sequence as inputs to predict the intermediate five frames.
Detailed Structure of Backbones.
C.1 Backbone for STVSR Task: RSTT
The Real-time Spatial Temporal Transformer (RSTT) [12] is cascaded UNet-style architecture for joint temporal interpolation and spatial super-resolution. consists four encoder stages Ex and four corresponding decoder stages D& (k- = 0, 1, 3,)
Given four consecutive low-resolution, low-frame-rate input frames ( 1 12 121+ 1 12tss the encoders extract multi-scale
features, which are stored as dictionaries. A query builder generates seven queries {Q1, Q...... Q7} used to synthesize the target high-resolution, eigh-frame-rade frames via the decoder stages.
Encoder Block. Each encoder block consists of two sub-blocks: Window-basee Multi-head Self Attention (W-MSA) block and Shifted Window-based Multi-head Self Attention (SW-MSA) block. Each sub- block is wrapped with Layer Normalization (LN), multi-layer perceptron (MLP), and residual connections.
The computation in each encoder block is:
(-M)A(LN(X)) + X, X MLP(LN(X)) + X, SW-MSA(LN(X)) + X, X = MLP(LN(X)) +X.
(10)
(11)
(12)
(13)
For a windowed feature X RMMx xc attention computed as:
QK
Attention( Q, K, V) Softmax
== x= K= xW V XWV. (14)
vd
Decoder Block, Each decoder block shares the same structure as the encoder block, except that self-attention is replaced by cross-attention. Specifically, W-MSA and SW-MSA are replaced by: Window-based Multi-head Cross Attention (W-MCA) and Shifted Window-based Multi-head Cross Attention (SW-MCA) The cross-attention computed as:
QK
CrossAttention(() (2, K, V) Softmax
V.
(15)
In this case, the query comes from the decoder input (or previous output), while the key and value (K, V) are obtained from the encoder dictionaries at the corresponding level.
C.2 Backbone for VSR Task: MIA
The overall structure of MIA-VSR [88] follows the bi-directional second-order grid propagation framework in BasicVSR++ [6]. The network consists of three main parts: feature extraction. feature propagation, and feature reconstruction. The feature extraction module uses convolution extract shallow features from the input low-resolution (LR) frames. The featurre reconstruction module adopts pixel-shuffle layer to upsample and reconstruct the high-resolution (HR) frames. The core of the network lies in the middle: the feature propagation module, where the MIA blocks are applied to recurrently enhance features across frames. Each feature propagation module Attention Blocks (IIABs). For video
(FPM) consists of N cascaded Masked Inter&Intra-fiit frame I, the m-th FPM receives the current input X and the enhanced features from previous frames (-1 (-2 m++1 m- to produce the updated feature X
m+
X F X!-1 X xt-2
21)
(16)
Each block inside FPM performs the following update:
X
IIAB
X
Xt
m+11 xt-2),
(17)
m,++1
m
26

---

where n .....,N 1 and X m_n -X
m The attention mechanism is used inside each IABB block. Instead of computing joint self-attention over concatenated features, MIA decouples attention into intra-frame and inter-frame branches. Specifically: The Query Qm., is generated only from the current feature X X, The Keys and Values contain both intra-frame tokens (from X m.n/ and inter-frame tokens (from X! x) The QKV formulation in the IIAB block is follows:
Qm,n = m.n Wº
K
[x m.n
W X X X W m+1i
(18)
V m,n
[X W [x x X WV1
m+1
where Wº, W WV are learnable projection matrices. The attention output is computed as:
Pim,n Kin.KKnm mnn
Attn Softmax
B
V m.n!
(19)
where B is learnable relative positional bias.
The output then passed through feedforward network (FFN) with LayerNorm and residual connections as in standard Transformer blocks.
C.3 Backhone for VFI Task: EMA-VFI
The overall architecture of EMA-VFI [78] consists of three main stages: (1) a CNN-based multi- scale feature extractor, (2) Transformer-
nptaon-appearance feature extractor, and (3) lightweight flow estimation module. The FlowEstimation module takes motion features Mo-+1 Mi-0 and appearance features Au. A₁ from the Transformer, and outputs refined flows Fi-o- Fi along with blending mask O to synthesize the intermediate frame.
Transformer Block. Each Transformer block is designed jointly extract appearance and motion features using fraer-frame attention. The block follows the standard Transformer layout with two residual sub-layers: an attention module and feed-forward network (FFN):
= X + Interfrraaeetttemmmmiooo(XXX)))) X out = X' FFN(LM(XX)),
(20)
(21)
where is the input token feature derived from either In or Jı. Inter-frame Attention. The attention is computed between a patch in to and its spatial neighbors in 4, formulated as:
Qú, = A WQ
(22)
K Kn(i,) A WK,
A Ar(ti)y
Vu(i.))
(23)
Qua K m(is))
5 Softmax
(24)
vd
where n(i,j) denotes the spatial neighborhood around position (i.j). Feed-forward Network (FFN). The FFN module incorporates desth-wise convolution to enhance spatial modeling:
FFN(X) = DWCOn)(EELU(XW:)))
(25)
where DWConv denotes a depth-wise convolution operator.
The Transformer blocks are stacked to extract hierarchical motion and appearance features from multi-scale CNN features, providing rich spatiotemporal representations for accurate intermediate frame synthesis.
27

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of
Retur
Figure 6: Per-frame activation statisties from Transformer layers. Each curve corresponds different frame in the input tensor.
Table 5: Quantitative comparison of different methods on four STVSR benchmarks under 6-bit quantization. The best and second best results are in bold and bold.
Vid4
Vimeo-Fast PSNR
Vimeo-Mediue PSNRT
Vimeo-Slow
Method
Bit
PSNR1
SSIM
SSIM
SSIM
PSNR1
SSIM
OpenVINO [15]
6/6
25.78
0.7692
35.49
0.9279
34.60
0.9259
32.67
0.8985
TensorRT [65]
6/6
25.71
0.7690
35.47
0.9275
34.60
0.9254
32.60
0.8981
SNPE [21]
6/6
25.92
0.7735
35.86
0.9315
34.71
0,9272
32,74
0.9035
Percentile [29]
6/6
25.88
0.7732
35.66
0.9298
34.64
0,9269
32,73
0.9035
MinMax [23]
6/6
25.96
0.7742
35.92
0.9315
34.69
0.9267
32.69
0.9021
NoisyQuant [42]
6/6
25.92
0.7741
35,81
0.9317
34.75
0.9281
32.70
0.9021
DBDC+Pac [64]
6/6
26.05
0.7837
36.10
0.9338
34.94
0.9303
32.92
0.9068
2DQuant [40]
6/6
26.04
0.7819
36.12
0.9339
34.95
0,9302
32.93
0.9068
Ours
6/6
26.10
0.7850
36.21
0.9345
35.04
0.9311
33.00
0.9076
D More Visualizations about Inter-frame Differences.
To further support the observation of frame-wise representational disparity discussed in Section 3.2, we present additional visualizations of activation distributions and attention patterns across frames in multi-frame video enhancement model.
Figure 6 shows the per-frame activation value ranges collected from intermediate Transformer layers. We observe significant variation in both the minimum and maximum activation values across frames, indicating frame-dependent distribution shifts. The figure illustrates that the model dynamically attends to frames with varying intensity, further confirming that representational capacity is not uniformly allocated. These observations justify the need for frame-aware quantization strategies that account for inter-frame variation in activation statistics and attention dynamics.
E More Quantitative Results under additional bit-width
Table 5 presents the quantitative comparison of various quantization methods on four STVSR benchmarks under 6-bit quantization. As shown, our method consistently outperforms all existing approaches across all datasets in terms of both PSNR and SSIM.
28

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F
More Visual Comparisons
To further validate the visual effectiveness of our method, we present additional qualitative compar- isons under 4-bit post-training quantization Figure 7. The figure includes challenging examples from three representative video enhancement tasks: Space-Time Video Super-Resolution (STVSR), Video Super-Resolution (VSR), and Video Frame Interpolation (VFI).
As shown in the figure, traditional methods such as MinMax [23] and Percentile [29] often result in oversmoothed regions noticeable artifacts. More advanced baselines like DBDC+Pac [64] and 2dquant [40] provide better structure but still suffer from detail loss, especially in textured or high-motion areas. contrast, our method produces sharper edges and more faithful textures that are visually closer to the full-precisio (FP) results.
These results further support the claim that quantization approach better preserves perceptual quality across wide range scenes and motion patterns.
GT
Tersont
MinMax
Percentile
DBDC+Pac
2douunnt
Ours
EP
GT
Tensont
MinMax
Percentle
DBDC+Pac
2douant
Ours
EP
GT
Tersont
MinMax
Percentile
Pac
2dquant
Ours
НР
Figure 7: Additional visual comparisons under 4-bit quantization for three video enhancement tasks: STVSR, VSR, and VFI. Our method consistently produces outputs closer the ground truth (GT) and full-precision (FP) results.
29

---

Table 6: Quantitative results under the 2-bit setting. PSNR and SSIM values reported for multiple benchmark datasets. The best and the second best results are bold and bold.
Set5 (x2)
Set14 (x2)
B100 (x2)
Urbun (x2)
Mangu109 (x2)
Method
Bit
PSNR
SSIM
PSNR
SSIMI
PSNR
SSIMI
PSNR
SSIMI
PSNR
SSIMI
SwinlR-light [35]
32
38.15
0.9611
33.86
0.9206
32.31
0.9012
32.76
0.9340
39.11
0.9781
Bicubic
32
32.25
0.9118
29.25
0.8406
28.68
0.8104
25.96
0.8088
29.17
0.9128
MinMax [23]
33.88
0.8951
30.05
0.8428
29.99
0.7232
26,30
0.7378
32.57
0.9446
Percentile [29]
2
34.35
0.9015
30.55
0.8466
30.45
0.7276
26.73
0.7464
33.10
0.9502
DRDC+Pac [64]
2
34.55
0.9052
30.65
0.8487
30.55
0.7291
26.85
0.7493
33.25
0.9529
DOBI [40]
2
35.25
0.9361
31.72
0.8917
30.62
0.8699
28.52
0.8727
33.65
0,9529
2DQuant [40]
2
36.00
0.9497
31.98
0.9012
30.91
0,8810
28.62
0.8819
34,40
0.9602
BTBI+PMTD
36.07
0.9501
32.04
0.9014
31.00
0.8821
28.68
0.8905
34.45
0,9615
G
User Stduy
To evaluate the visual quality of the image super-resolution results, we conducted a user study involving 15 participants. Fifteen im- ages were randomly selected from the test datasets, and the participants were asked to rate the quality each processed image on scale from (poor quality) to 10 (excellent Percentile
Tensorrt
Aver4
Ave:5.0
quality). The aggregated results, as illustrated the figure, indicate that existing methods DBDC+Pac struggle to fully restore image quality, result- ing in lower user satisfaction. In contrast, method achieves the highest average score 8.1, significantly outperforming other meth- ods and demonstrating superior capabilities in visual performance and generalization.
Ave.6
2dquant
Ave:7.0
Ours
Ave:8.1
Figure 8: User study on image visual quality.
More Ablation Studys
In this section, we provide additional ablation studies verify the effectiveness of our proposed method. Specifically, we replace the core components of 2DQuant [40] with our approach. That is. we replace DOBI with BTBI and DQC with our multi-teacher distillation network. Following the experimental setup in 2DQuant, we conduct experiments under the 2-bit setting. The results demonstrate that our method achieves significant improvements over the baseline, as shown in Table 6.
30


Reviews:
[{"review_id": "mKNZtyR6oR", "reviewer": "Reviewer_wyAz", "summary": "Transformer-based video enhancement models are too resource-intensive for edge deployment, while traditional quantization methods fail because they cannot handle varying data distributions across frames or the large representation gap between teacher and student models. To solve this, this paper proposes a novel two-stage method PMQ-VE, containing the Backtracking-based Multi-Frame Quantization (BMFQ) and the Progressive Multi-Teacher Distillation (PMTD). Experiments show that PMQ-VE achieves state-of-the-art performance on multiple benchmarks.", "questions": "1. Could you further analyze and discuss the computational overhead of the BTBI module, particularly in comparison to baseline methods?\n\n2. Could you comment on the stability and reliability of the results, and elaborate on why the substantial performance gains are reported from a single run?\n\n3. Could you elaborate on the selection of the new hyperparameters and discuss the expected sensitivity of the framework's performance to their variations?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. This paper investigates and solves the problem of quantizing multi-frame video enhancement models. This is a practical and important problem, as the efficient deployment of video models is a key bottleneck for many real-world applications. \n\n2. The proposed PMQ-VE framework has two core components, BMFQ and PMTD, effectively addressing the corresponding challenges.\n\n3. The results consistently demonstrate the superiority of the proposed method, with significant gains in PSNR/SSIM on multiple datasets.\n\n4. The paper is well-written and easy to understand. \n\nWeaknesses:\n\n1. The paper lacks detailed quantitative analysis and proof of the computational cost of proposed modules such as BTBI, and lacks comparison with other baseline methods.\n\n2. All the experimental results reported in this paper are the results of a single experiment, rather than the standard deviations and means of multiple experiments. While the performance gains are substantial, reporting statistics over multiple independent experiments can demonstrate the stability and reliability of the results.\n\n3. The proposed framework introduces new hyperparameters. However, the paper does not provide a sensitivity analysis for these parameters, making it difficult to assess the method's robustness or how to tune them for new scenarios.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewers' recognition of the novelty, practicality, and effectiveness of our proposed PMQ-VE framework for quantization in multi-frame video enhancement, as well as its strong performance across various benchmarks and the clarity of our writing.\n\n> **Q1:** Lack of quantitative analysis and comparison of computational cost for BTBI and baseline methods\n\n**A1:** Following your advice, we evaluate the cost time, number of search iterations, and final performance (PSNR) on multiple STVSR benchmarks under both 2-bit and 4-bit settings. As shown in Table 1, our BTBI method achieves significantly lower running time compared to other search-based methods (e.g., DOBI from 2DQuant, DBDC), while achieveing the best overall performance.\nThis efficiency stems from two key design: (1) a tightly bounded search space based on percentiles, and (2) an early-pruning mechanism that eliminates redundant evaluations. Although BTBI performs more iterations due to per-frame bound search, this is a one-time offline process. During inference, the model uses precomputed bounds like all other PTQ methods, incurring no additional running time or memory overhead.\n\n\n#### Table 1: Performance and Computational Cost Comparison of BTBI on STVSR (5-run average ± std)\n| Method          | Bit   | Cost Time       | iterations      | **Vid4**       | **Vimeo-Fast** | **Vimeo-Medium** | **Vimeo-Slow** |\n| --------------- | ----- | --------------- | --------------- | -------------- | -------------- | ---------------- | -------------- |\n| RSTT-S(FP)      | 32/32 | N/A             | N/A             | 26.29          | 36.58          | 35.43            | 33.30          |\n| Percentile      | 2/2   | N/A             | N/A             | 12.64±0.29     | 15.23±0.24     | 14.82±0.18       | 14.67±0.21     |\n| MinMax          | 2/2   | N/A             | N/A             | 10.44±1.19     | 10.54±0.25     | 10.36±0.16       | 10.21±0.41     |\n| DOBI            | 2/2   | 118.98s±1.23s   | 400.00±0.00     | 20.15±0.14     | 23.19±0.13     | 23.33±0.09       | 23.59±0.18     |\n| DBDC            | 2/2   | 173.37±1.72s    | 153.73±20.47    | 18.95±0.35     | 23.49±0.14     | 23.33±0.11       | 23.49±0.19     |\n| **BTBI (Ours)** | 2/2   | **70.89±0.57s** | **410.52±8.42** | **21.45±0.12** | **24.63±0.12** | **25.28±0.12**   | **25.96±0.20** |\n| Percentile      | 4/4   | N/A             | N/A             | 23.14±0.21     | 27.09±0.11     | 26.96±0.34       | 26.59±0.51     |\n| MinMax          | 4/4   | N/A             | N/A             | 22.67±0.68     | 26.45±0.13     | 25.87±0.46       | 25.47±0.51     |\n| DOBI            | 4/4   | 130.17s±7.23s   | 400.00±0.00     | 23.95±0.12     | 31.59±0.05     | 29.93±0.31       | 30.59±0.31     |\n| DBDC            | 4/4   | 200.47±30.72s   | 167.73±18.96    | 23.72±0.19     | 30.41±0.15     | 29.98±0.24       | 30.54±0.51     |\n| **BTBI (Ours)** | 4/4   | **80.89±1.98s** | **421.45±7.31** | **24.15±0.11** | **32.24±0.12** | **30.77±0.23**   | **30.66±0.21** |\n\n\n> **Q2:** Lack of statistical reporting across multiple runs; concerns about stability and reproducibility\n \n**A2:** As shown in Tables 2, 3, and 4, we conduct five independent runs of our method on all three tasks (STVSR, VFI, and VSR), each with a different random seed and calibration set. The resulting PSNR values and their standard deviations (typically < ±0.1 dB) demonstrate the stability, robustness, and reproducibility of our method under low-bit quantization.\n\n#### Table 2: STVSR (5-run average ± std)\n| Bit | Vid4         |                 | Vimeo-Fast   |                 | Vimeo-Medium |                 | Vimeo-Slow   |                 |\n| --- | ------------ | --------------- | ------------ | --------------- | ------------ | --------------- | ------------ | --------------- |\n|     | PSNR         | SSIM            | PSNR         | SSIM            | PSNR         | SSIM            | PSNR         | SSIM            |\n| 2/2 | 23.45 ± 0.06 | 0.6214 ± 0.0014 | 30.34 ± 0.05 | 0.8442 ± 0.0013 | 30.17 ± 0.06 | 0.8543 ± 0.0013 | 29.11 ± 0.06 | 0.8315 ± 0.0014 |\n| 4/4 | 25.42 ± 0.05 | 0.7542 ± 0.0023 | 34.66 ± 0.08 | 0.9165 ± 0.0012 | 33.76 ± 0.07 | 0.9117 ± 0.0023 | 31.93 ± 0.06 | 0.8938 ± 0.0013 |\n\n#### Table 3: VFI (5-run average ± std)\n| Bit | EMA-VFI [T]  |                 |               |             | EMA-VFI [D]  |                 |               |             |\n| --- | ------------ | --------------- | ------------- | ----------- | ------------ | --------------- | ------------- | ----------- |\n|     | PSNR         | SSIM            | LPIPS         | NIQE        | PSNR         | SSIM            | LPIPS         | NIQE        |\n| 4/4 | 28.41 ± 0.04 | 0.9152 ± 0.0022 | 0.131 ± 0.003 | 7.34 ± 0.05 | 29.57 ± 0.03 | 0.9362 ± 0.0012 | 0.101 ± 0.002 | 6.74 ± 0.03 |\n\n#### Table 4: VSR (5-run average ± std)\n| Bit | Vimeo90K     |                 | Vid4         |                 |\n| --- | ------------ | --------------- | ------------ | --------------- |\n|     | PSNR         | SSIM            | PSNR         | SSIM            |\n| 4/4 | 37.32 ± 0.05 | 0.9442 ± 0.0012 | 27.65 ± 0.04 | 0.8350 ± 0.0020 |\n\n> **Q3:** Sensitivity of newly introduced hyperparameters and their impact on robustness\n\n**A3:** We conducted a sensitivity analysis on the key hyperparameters of PMQ-VE, including:\n- The distillation loss weight λ  \n- The number of BTBI search steps N (which controls ΔL/ΔU)  \n- The early-stopping threshold ε  \n- The percentile search ranges for `lb` and `ub`\nWe varied each parameter individually while keeping the others fixed, and evaluated the resulting performance under 4-bit quantization. As shown in Table 5, across all variations, the PSNR remains within ±0.1 dB of the default setting, demonstrating that our method is robust to reasonable changes in hyperparameter configurations. These minimal fluctuations confirm that our coarse-to-fine framework is robust to reasonable changes in hyperparameter configurations.\n\n#### Table 5: Hyperparameter Sensitivity Analysis Under 4-bit Quantization (5-run average ± std)\n| Variant          | N   | ε    | Range                    | λ   | BTBI Time   | BTBI Iters   | **Vid4**   | **Vimeo-Fast** | **Vimeo-Medium** | **Vimeo-Slow** |\n| ---------------- | --- | ---- | ------------------------ | --- | ----------- | ------------ | ---------- | -------------- | ---------------- | -------------- |\n| **Default**      | 10  | 5e-4 | [0.1, 10] / [90, 99.9]   | 5   | 80.89±1.98s | 421.45±7.31  | 25.42±0.02 | 34.66±0.03     | 33.76±0.05       | 31.93±0.04     |\n| N ↑              | 11  | 5e-4 | [0.1, 10] / [90, 99.9]   | 5   | 81.45±2.45s | 432.46±8.54  | 25.42±0.08 | 34.68±0.05     | 33.79±0.04       | 31.92±0.06     |\n| N ↓              | 9   | 5e-4 | [0.1, 10] / [90, 99.9]   | 5   | 80.23±3.44  | 420.31±8.91  | 25.41±0.05 | 34.66±0.06     | 33.75±0.07       | 31.90±0.01     |\n| ε ↑              | 10  | 6e-4 | [0.1, 10] / [90, 99.9]   | 5   | 81.35±2.64  | 432.55±6.88  | 25.40±0.04 | 34.65±0.06     | 33.74±0.06       | 31.92±0.04     |\n| ε ↓              | 10  | 4e-4 | [0.1, 10] / [90, 99.9]   | 5   | 79.91±3.41  | 401.75±8.79  | 25.41±0.04 | 34.67±0.04     | 33.75±0.04       | 31.92±0.03     |\n| Wider Range ↑    | 10  | 5e-4 | [0.01, 15] / [85, 99.99] | 5   | 83.89±2.54  | 434.65±6.45  | 25.38±0.03 | 34.61±0.06     | 33.71±0.04       | 31.89±0.02     |\n| Narrower Range ↓ | 10  | 5e-4 | [0.1, 1] / [99, 99.9]    | 5   | 82.84±12.54 | 430.22±30.45 | 25.41±0.06 | 34.59±0.03     | 33.69±0.04       | 31.90±0.02     |\n| λ ↑              | 10  | 5e-4 | [0.1, 10] / [90, 99.9]   | 5.5 | 80.89±5.54  | 421.45±10.41 | 25.42±0.01 | 34.67±0.06     | 33.73±0.03       | 31.93±0.05     |\n| λ ↓              | 10  | 5e-4 | [0.1, 10] / [90, 99.9]   | 4.5 | 80.89±3.48  | 421.45±9.10  | 25.41±0.06 | 34.66±0.05     | 33.75±0.04       | 31.93±0.05     |\n\n\nWe greatly appreciate the reviewer’s valuable suggestions. All raised concerns—including computational analysis, statistical reporting, and hyperparameter sensitivity—will be addressed in the final version to enhance the completeness and clarity of our submission."}, {"review_id": "NZq1SvcwQj", "reviewer": "Reviewer_LizY", "summary": "The authors propose a multi-frame video enhancement method relying on two underlying concepts - a)Backtracking based quantization - involves frame based adaptive quantization where the quantization bounds are obtained adaptively for every frame using backtracking, b) Progressive multi-teacher distillation - this framework aims to reproduce representational capacity of full-precision teacher by employing a progressive training scheme with intermediate 8/4-bit teacher. Extensive experiments across 3 tasks : Video Frame Interpolation (VFI), Video Super-Resolution (VSR), and Spatio-Temporal Video Super-Resolution (STVSR) are performed. The results show improvement over existing state-of-the-art low-bit models.", "questions": "1. Although the backtracking based quantization is proposed for frame based manner from the implementation details it appears to be done at a crop/patch level. Just wondering if spatial dimensions have any impact? Additionally if instead of doing backtracking in a patch-based manner instead of frame level are there any pros/cons associated with it?\n\n2. What is the average recursion depth observed in the training? Can that be reduced by leveraging certain assumptions such as the motion does not change significantly between consecutive frames?", "limitations": "As the authors have pointed out, diffusion models aren't included in this work. It will interesting to know how they perform with this technique.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The idea of adaptively modifying quantization for each frame using backtracking is a novel idea and hasn't been explored earlier. Also from the ablation experiments it is evident that this provides the highest performance boost.\n\n2. Progressive quantization is a simple but powerful idea where distillation is done to very low-bits in a progressive manner. From the results this also leads to better performance. \n\n3. The paper is presented in an elegant manner and the algorithm is easy to comprehend. Overall the proposed technique improves upon the existing state-of-the-art and is generalizable across different video enhancement tasks as well as can be applied across different deep learning models.\n\n\nWeaknesses\n1. The quantization scheme proposed involves a recursive backtracking strategy performed on a frame level manner. This could be compute heavy for videos with large number of frames and/or with high resolution. Also authors haven't reported the computational complexity of their method, so hard to understand how different video characteristics impact on the compute.\n\n2. Although the authors propose the above method for video enhancement there isn't any specific information associated with enhancement (except it is applied for each frame) that is employed in the design. Perhaps the same design can be applicable to other tasks as well? For example in videos there is significant overlap between temporal information between successive frames. This could be employed to reduce the recursive search as doing backtracking based search for every video frame could be wasteful as well as expensive.", "was_revised": true, "final_justification": "The authors have reasonably addressed the concerns. Since I had a high rating before I am keeping the same decision.", "rebuttal": "We appreciate the reviewer’s recognition of our work, particularly the novelty of our frame-wise backtracking quantization strategy, the effectiveness and simplicity of the progressive multi-teacher distillation framework, and the strong empirical results across VFI, VSR, and STVSR tasks. We are also grateful for the positive feedback regarding the clarity and generalizability of our approach.\n> **Q1:** Computational Complexity and Average recursion Iterations of the BTBI Module and other baseline methods\n\n**A1:** Following your advice, we compare the average number of iterations and the running time of our BTBI method with other baseline methods. As shown in Table 1, while our per-frame backtracking strategy introduces more iterations due to per-frame bound search, the actual runtime remains significantly lower than existing search-based approaches(e.g., DOBI from 2DQuant, DBDC). This efficiency is mainly attributed to our percentile-constrained search space and early-pruning design, which effectively narrows down the candidate bounds and avoids unnecessary evaluations.\nNotably, this overhead is only introduced once during the offline quantization phase. During inference, BTBI shares the same runtime cost as other post-training quantization methods, as it simply uses the precomputed quantization parameters without any additional computation, regardless of video length or resolution.\n\n#### Table 1: Performance and Computational Cost Comparison of BTBI on STVSR \n| Method          | Bit   | Cost Time  | Iterations | **Vid4**  | **Vimeo-Fast** | **Vimeo-Medium** | **Vimeo-Slow** |\n| --------------- | ----- | ---------- | ---------- | --------- | -------------- | ---------------- | -------------- |\n| RSTT-S (FP)     | 32/32 | N/A        | N/A        | 26.29     | 36.58          | 35.43            | 33.30          |\n| Percentile      | 2/2   | N/A        | N/A        | 12.64     | 15.23          | 14.82            | 14.67          |\n| MinMax          | 2/2   | N/A        | N/A        | 10.44     | 10.54          | 10.36            | 10.21          |\n| DOBI            | 2/2   | 118.98s    | 400.00     | 20.15     | 23.19          | 23.33            | 23.59          |\n| DBDC            | 2/2   | 173.37s    | 153.73     | 18.95     | 23.49          | 23.33            | 23.49          |\n| **BTBI (Ours)** | 2/2   | **70.89s** | **410.52** | **21.45** | **24.63**      | **25.28**        | **25.96**      |\n| Percentile      | 4/4   | N/A        | N/A        | 23.14     | 27.09          | 26.96            | 26.59          |\n| MinMax          | 4/4   | N/A        | N/A        | 22.67     | 26.45          | 25.87            | 25.47          |\n| DOBI            | 4/4   | 130.17s    | 400.00     | 23.95     | 31.59          | 29.93            | 30.59          |\n| DBDC            | 4/4   | 200.47s    | 167.73     | 23.72     | 30.41          | 29.98            | 30.54          |\n| **BTBI (Ours)** | 4/4   | **80.89s** | **421.45** | **24.15** | **32.24**      | **30.77**        | **30.66**      |\n\n\n> **Q2:** Task-Specificity of PMQ-VE\n\n**A2:** We agree with the reviewer that our method is general and can be applied to other tasks beyond video enhancement.\nIn this paper, we focus on three representative video enhancement tasks—VFI, VSR, and STVSR—because they cover the core challenges in video enhancement:\n- VFI captures temporal dependencies,\n- VSR focuses on spatial reconstruction,\n- STVSR requires joint spatio-temporal modeling.\n\nThese tasks are not only fundamental but also diverse in their modeling requirements, making them ideal benchmarks to validate the generality and robustness of our quantization framework. Since most other video tasks also rely on spatial and/or temporal cues, our method can be naturally extended to them.\n\n> **Q3:** Whether Spatial Resolution (Patch vs. Frame level) Affects the Quantization Process  \n\n**A3:**  Spatial dimensions do not significantly impact the quantization process in our framework. Although we use patch-based augmentation(e.g., cropping) during training, quantization is consistently performed at the frame level. At inference, the learned per-frame clipping bounds are applied to full-resolution frames, and this works well because normalization layers (e.g., LayerNorm) stabilize activation distributions across spatial scales. As a result, whether the input is a full frame or a patch, activations are mapped to a consistent range, ensuring quantization stability.\n\nWe do not adopt patch-level quantization because it would require modifying the inference pipeline, adding unnecessary complexity and inconsistency with training.\n\n\n> **Q4:** Leveraging Temporal Redundancy to Reduce Recursive Search Overhead in BTBI \n\n**A4:** Thank you for the insightful suggestion. We agree that consecutive video frames share considerable temporal redundancy, and this can be leveraged to reduce the computational cost of the per-frame recursive bound search in BTBI.\nTo address this, we implemented a **temporal warm-start strategy**, where the BTBI search for frame *i* is initialized using the optimal bounds obtained from the previous frame *i−1*. \nBelow is a simplified illustration of the idea:\n```python\n# Temporal warm-start BTBI\nfor i in range(T):\n    if i == 0:\n        lb_i, ub_i = BTBI_search(X[0])  # standard search\n    else:\n        lb_i, ub_i = BTBI_search(X[i], init=(lb_{i−1}, ub_{i−1}))  # warm start\n```\nWe compare the original BTBI and the warm-start variant in the table2. As shown, the warm-start version substantially reduces both the average iteration count and runtime cost, while maintaining nearly identical performance in terms of PSNR.\nThis warm-start strategy brings consistent improvements in both runtime and iteration number, with negligible impact on final restoration quality. It demonstrates that leveraging temporal continuity is an effective way to reduce BTBI overhead in real video applications.\n\nWe sincerely thank the reviewer for this excellent suggestion, which directly helped us improve the efficiency of the BTBI module. We will include this strategy, its implementation, and the empirical results in the final version of the paper.\n\n#### Table 2: Effect of Warm-Start Strategy on BTBI (2-bit and 4-bit Quantization)\n| Method          | Bit | BTBI Cost Time | iterations | **Vid4** | **Fast** | **Medium** | **Slow** |\n| --------------- | --- | -------------- | ---------- | -------- | -------- | ---------- | -------- |\n| BTBI            | 2/2 | 70.89          | 410.52     | 21.45    | 24.63    | 25.28      | 25.96    |\n| BTBI+warm-start | 2/2 | 63.22          | 298.91     | 21.43    | 24.62    | 25.26      | 25.94    |\n| BTBI            | 4/4 | 80.89          | 421.45     | 24.15    | 32.24    | 30.77      | 30.66    |\n| BTBI+warm-start | 4/4 | 72.21          | 312.67     | 24.13    | 32.23    | 30.75      | 30.65    |\n\nWe greatly appreciate the reviewer’s thoughtful and constructive feedback. All raised concerns—including BTBI’s computational overhead, task generality, spatial resolution effects, and the suggestion to leverage temporal redundancy—will be further clarified in the final version to strengthen the technical clarity and  quanlity of our work."}, {"review_id": "oW5c5oLKL4", "reviewer": "Reviewer_7rvP", "summary": "This paper proposes PMQ-VE, a novel two-stage quantization framework for multi-frame video enhancement tasks. PMQ-VE introduces Backtracking-based Multi-Frame Quantization to search frame-specific clipping bounds via percentile initialization and backtracking search, and Progressive Multi-Teacher Distillation to leverage full-precision and intermediate-bit teachers for knowledge transfer. Experiments show PMQ-VE outperforms SOTA methods across benchmarks under low-bit quantization.", "questions": "1. The main problem is: Is BMFQ performed during the training phase (i.e., QAT, to learn frame-specific bounds for the training set) or during inference for each test frame (i.e., PTQ)? I suppose BMFQ might be a QAT strategy, but the authors have not explicitly pointed this out. If it is training-phase only, it seems that you are overfitting lb and ub parameters for each specific frame in the training set, then how do the learned bounds generalize to unseen test frames with different distributions? If it's done at test phase, it means you are quantizing a full-precision model during the test phase. Since this full-precision model, which can lead to better image quality than the quantized model, can be used for inference during testing, then what's the point and meaning of doing quantization? Additionally, if it's done at test phase, how much is the computational cost? Please clarify the workflow.\n\n2. The paper claims BTBI has \"negligible overhead,\" but please provide quantitative comparisons (e.g., training iterations, GPU memory usage, inference latency) between PMQ-VE and baseline methods to validate this. If overhead exists, how does it impact edge deployment scenarios where efficiency is critical?\n\n3. In PMTD, how are the intermediate-bit teachers trained? Are they quantized from the full-precision model using the same BMFQ strategy, or via other methods?\n\nI'm not sure if my understanding is entirely correct so far. If the authors can address my concerns well, I might increase my evaluation score; otherwise, I will still tend to reject the manuscript", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: 1. The novel BMFQ and PMTD strategies address the challenges in video enhancement quantization. 2. The authors conducted comprehensive experiments and ablation studies on different tasks and benchmarks, which demonstrates quantitative and qualitative improvements.\n\nWeakness\n1.The main problem is the clarity of BMFQ implementation. The motivation of BMFQ is also not well introduced. The paper lacks explanation on whether BMFQ is applied during training or testing, and how frame-specific bounds are generalized to unseen test frames (if BMFQ is applied to the training set). This ambiguity causes lots of confusion about the practical deployment workflow (e.g., if per-frame boundary search is required for each test input, it may introduce considerable inference computational cost; if the per-frame boundary search is done for each training input, the parameters would not be suitable for the test frames).\n2. Besides, the backtracking search in BMFQ is described as \"negligible overhead,\" but the paper does not provide concrete data (e.g., training/inference time increase, memory usage) to support this claim, which is critical for evaluating practical efficiency.", "was_revised": true, "final_justification": "The authors have cleared up my misunderstanding and addressed my concerns during the rebuttal. The proposed approach now seems technically novel and sound. I suggest that the authors improve the clarity of the writing and incorporate some key issues we discussed into the final version.", "rebuttal": "We appreciate the reviewer’s recognition of our novel quantization strategies and the effectiveness of our empirical validation across multiple video enhancement tasks. In response to the reviewer’s questions, we provide the following clarifications:\n\n> **Q1:** What's the point and meaning of doing quantization? Is BMFQ a PTQ or QAT method?  \n\n**A1:**  Quantization reduces model size and inference cost by converting high-precision computations into low-precision ones, enabling efficient deployment without significant accuracy loss. Our approach is **Post-Training Quantization (PTQ)**. Specifically, we apply BMFQ and PMTD using unlabeled data, without updating the model weights. The purpose is to learn quantization parameters for both weights and activations. Consistent with other PTQ methods, our approach does not run during inference, ensuring no additional overhead at deployment time.\n\nTo clarify:\n- QAT updates both weights and quantization parameters during training.\n- PTQ keeps weights fixed and only learns clipping bounds.\n\nQAT provides higher accuracy by simulating quantization during training, but it requires retraining and more resources. PTQ is simpler and faster to apply, making it suitable for efficient deployment without modifying the original training pipeline — that’s why we choose PTQ.\n\n> **Q2:** How frame-specific bounds are generalized to unseen test frames? \n\n**A2:** We'd like to clarify that our approach follows the standard practice of all PTQ methods, where quantization parameters (e.g., clipping bounds) are learned once offline from a calibration set and then frozen for inference, This is a widely adopted paradigm in PTQ literature [1, 2, 3] \n\nRegarding generalization to unseen frames, we note that activation ranges are generally stable across samples, thanks in large part to the use of normalization layers (e.g., LayerNorm) in Transformer-based video enhancement models. While the distribution shape of activations may vary depending on content or motion, the value range—which is the key factor for quantization—remains relatively consistent across different samples. \n\nSince these ranges are primarily determined by the  model architecture and its pretrained parameters—rather than by the raw input pixels or the specific temporal position of a frame—they tend to remain consistent across different sequences. This enables the learned clipping bounds to generalize well to unseen data.\n\n> **Q3:** The motivation of BMFQ is not well introduced\n\n**A3:**  We would like to clarify that this has been discussed in detail in Lines 152–168 of the main paper. The motivation for proposing Backtracking-based Multi-Frame Quantization (BMFQ) stems from two key challenges in quantizing multi-frame video enhancement models:\n1. Per-frame activation variation:  \n   As shown in Figure 2-a of our paper, we collect per-frame activation statistics and observe significant disparities in activation distributions across frames. Traditional quantization methods — using shared clipping bounds across frames — fail to capture this variation, leading to suboptimal quantization and degraded reconstruction quality. BMFQ addresses this by introducing frame-specific bounds, allowing for better adaptation to heterogeneous activation statistics.\n2. Robust and efficient bound initialization:  \n   Our backtracking strategy refines the initialization by recursively searching within a constrained percentile range. This approach avoids exhaustive scanning and ensures high-quality quantization with negligible training overhead.\n\nAdditionally, as demonstrated in our ablation study, directly applying PMTD from a naive min-max initialization leads to suboptimal results. In contrast, using BMFQ provides a reliable starting point for PMTD with only ~1 minute of initialization time, significantly improving training efficiency and final model quality.\n\n\n\n> **Q4:** Computational Analysis of the BTBI Module and other baselines\n\n**A4:** We evaluate the cost time, number of search iterations, and final performance (PSNR) on multiple STVSR benchmarks under both 2-bit and 4-bit settings. As shown in the Table 1, our BTBI method achieves significantly lower runtime compared to other methods such as DOBI(2dquant) and DBDC, while delivering the best overall performance. This efficiency stems from two key design choices:A tightly bounded search space based on percentiles and An early-pruning strategy that avoids unnecessary evaluations.\nAlthough BTBI involves slightly more iterations (due to per-frame bound search), this is a one-time offline process. During inference, the model uses the precomputed quantization bounds just like other PTQ methods, introducing no additional runtime or memory overhead.\nThese results validate the Superiority of our approach: BTBI enables finer-grained per-frame quantization with minimal cost.\n\n#### Table 1: Performance and Computational Cost Comparison of BTBI on STVSR\n| Method      | Bit   | Cost Time  | Iterations | **Vid4**  | **Vimeo-Fast** | **Vimeo-Medium** | **Vimeo-Slow** |\n| ----------- | ----- | ---------- | ---------- | --------- | -------------- | ---------------- | -------------- |\n| RSTT-S (FP) | 32/32 | N/A        | N/A        | 26.29     | 36.58          | 35.43            | 33.30          |\n| Percentile  | 2/2   | N/A        | N/A        | 12.64     | 15.23          | 14.82            | 14.67          |\n| MinMax      | 2/2   | N/A        | N/A        | 10.44     | 10.54          | 10.36            | 10.21          |\n| DOBI        | 2/2   | 118.98s    | 400.00     | 20.15     | 23.19          | 23.33            | 23.59          |\n| DBDC        | 2/2   | 173.37s    | 153.73     | 18.95     | 23.49          | 23.33            | 23.49          |\n| **BTBI**    | 2/2   | **70.89s** | **410.52** | **21.45** | **24.63**      | **25.28**        | **25.96**      |\n| Percentile  | 4/4   | N/A        | N/A        | 23.14     | 27.09          | 26.96            | 26.59          |\n| MinMax      | 4/4   | N/A        | N/A        | 22.67     | 26.45          | 25.87            | 25.47          |\n| DOBI        | 4/4   | 130.17s    | 400.00     | 23.95     | 31.59          | 29.93            | 30.59          |\n| DBDC        | 4/4   | 200.47s    | 167.73     | 23.72     | 30.41          | 29.98            | 30.54          |\n| **BTBI**    | 4/4   | **80.89s** | **421.45** | **24.15** | **32.24**      | **30.77**        | **30.66**      |\n\n> **Q5:** Please clarify the workflow.\n\n**A5:** The workflow of PMQ-VE follows a simple and modular two-stage pipeline:\n1. Input: A pretrained full-precision (FP32) model.\n2. Stage 1 — Coarse (BMFQ):\nPerform per-frame clipping bound search using our Backtracking-based Multi-Frame Quantization (BMFQ), which computes robust activation ranges for each frame.\n3. Stage 2 — Fine (PMTD):\nApply Progressive Multi-Teacher Distillation (PMTD) to transfer knowledge from full-precision and intermediate-bit (e.g., 8/4-bit) teacher models to the low-bit student model (e.g., 4/2-bit).\n4. Output: A quantized model with integer weights and fixed activation bounds.\n5. Deployment and Inference: The model performs efficient integer matrix operations using precomputed quantization parameters, without any additional search or adaptation\n\n\n\n> **Q6:** How are the intermediate-bit teachers trained?\n\n**A6:** Teachers are progressively trained:\n- 8-bit model: Trained using BMFQ + distillation from FP32 supervision.\n- 4-bit model: Trained using BMFQ + distillation from both FP32 and 8-bit supervision.\n- 2-bit model: Trained using BMFQ + distillation from FP32, 8-bit, and 4-bit supervision.\nThis hierarchical supervision bridges the capacity gap between FP32 and low-bit models, and is validated through ablation studies (Table 4, Table 6) in our submission.\n\n\nWe noticed that several of your comments reflect a misunderstanding of some fundamental concepts underlying our work. Please feel free to reach out if anything remains unclear.\n\n[1] Chen Z, Qin H, Guo Y, et al. Binarized diffusion model for image super-resolution[J]. Advances in Neural Information Processing Systems, 2024, 37: 30651-30669.\n\n[2] Yuan Z, Xue C, Chen Y, et al. Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization[C]//European conference on computer vision. Cham: Springer Nature Switzerland, 2022: 191-207.\n\n[3] Tu Z, Hu J, Chen H, et al. Toward accurate post-training quantization for image super resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 5856-5865."}, {"review_id": "RI4B7QtE6o", "reviewer": "Reviewer_B6R2", "summary": "The authors propose Backtracking-based Multi-Frame Quantization, a simple but effective framework that addresses the limitations of traditional global quantization in multi-frame video enhancement. Besides, the authors propose PMTD, a method that leverages knowledge from multi-level teacher models to guide a low-bit student model, thereby improving its mapping quality and overall performance. The structure of the paper is clear, the motivation is explicit, and the experimental results demonstrate the effectiveness of the two innovations proposed by the authors.", "questions": "1. Instead of searching for the optimal upper and lower bounds for each frame, searching every few frames (because the spatial information of adjacent frames of the video is extremely close) can we further reduce the computational complexity of the method?\n2. What is the impact of varying the number of intermediate-bit teachers (e.g., using 6-bit alongside 8-bit) in the PMTD strategy on low-bit model performance?\n3. How does the percentile-based initialization in BMFQ specifically suppress outliers, and what percentile ranges are experimentally validated as optimal?", "limitations": "Not applicable.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper proposes a two-stage PMQ-VE framework and progressive multi-teacher distillation (PMTD) to address inter-frame distribution differences and capacity gaps in multi-frame video enhancement.\n2. The percentile-based initialization and backtracking search efficiently optimize frame-specific bounds with negligible overhead.\n3. Extensive experiments across STVSR, VSR, and VFI demonstrate their effectiveness.\n\nWeaknesses:\n1. The study lacks analysis of computational trade-offs across different quantization levels (e.g., 2-bit vs. 4-bit) for practical edge deployment scenarios.", "was_revised": true, "final_justification": "The author has excellently demonstrated through experiments the trade-off between different quantization levels and the number of quantization frames and the computational efficiency. After considering the opinions of other reviewers, I have decided to increase my score.", "rebuttal": "We appreciate the reviewer’s recognition of the novelty and effectiveness of our frame-wise quantization strategy and the progressive multi-teacher distillation framework. We also appreciate the positive feedback regarding the clarity of our paper and the strong experimental results demonstrated across STVSR, VSR, and VFI tasks.\n\n> **Q1:** Computational Trade-offs across Different Quantization Levels\n\n**A1:** We quantitatively analyze the trade-offs between bit-width, visual quality, model compression, and inference speed. As shown in Table 1, 4-bit quantization strikes a good balance,  achieving  \\~3×  compression, \\~1.5× speedup, and retaining over 96% of the full-precision PSNR. In contrast, 2-bit models offer the highest efficiency, but at the cost of a significant quality drop (\\~2.8dB PSNR loss).\n\n#### Table 1: Trade-offs Between Bit-width, Efficiency, and Quality (Vid4)\n| Bit-width | PSNR (Vid4) | SSIM (Vid4) | Compression ratio | Speedup ratio |\n| --------- | ----------- | ----------- | ----------------- | ------------- |\n| 32-bit    | 26.29       | 0.7941      | 1x                | 1x            |\n| 8-bit     | 26.24       | 0.7939      | 2.39x             | 1.22x         |\n| 4-bit     | 25.42       | 0.7501      | 3.11x             | 1.53x         |\n| 2-bit     | 23.48       | 0.6252      | 3.67x             | 2.01x         |\n\n> **Q2:** Reducing Complexity via Frame Grouping for Bound Search  \n\n**A2:** We conduct experiments by modifying the granularity of quantization bound search from per-frame to per-group, where one group contains multiple consecutive frames. Using our two-stage framework, we tested different group sizes (1, 2, 3, 4). In preliminary experiments, we observed that grouping frames and sharing quantization bounds within short temporal windows (e.g., every 2–3 frames) can reduce the computational overhead of BTBI with negligible impact on accuracy. However, for fast motion or complex scenes, where inter-frame differences are more significant, per-frame quantization still offers better robustness. The results (on STVSR task, 2-bit setting) are summarized in Table 2:\n\n#### Table 2: Impact of Frame Grouping on BTBI Efficiency and Quantization Performance\n| Group Size    | BTBI Cost Time | BTBI itertion | PSNR (Vid4) | PSNR (Vimeo-Fast) | PSNR (Vimeo-Medium) | PSNR (Vimeo-Slow) |\n| ------------- | -------------- | ------------- | ----------- | ----------------- | ------------------- | ----------------- |\n| 1 (per-frame) | 70.89s          | 410.52        | 23.48       | 30.33             | 30.19               | 29.14             |\n| 2             | 53.56s          | 369.32        | 23.36       | 30.11             | 30.13               | 29.10             |\n| 3             | 42.91s          | 301.54        | 23.11       | 30.02             | 30.00               | 28.99             |\n| 4             | 30.01s          | 241.98        | 22.99       | 29.48             | 29.71               | 28.78             |\n\n> **Q3:** Effect of Adding Intermediate-Bit Teachers in PMTD\n\n**A3:** We extended our PMTD framework by introducing a 6-bit teacher, trained via distillation from FP32 and 8-bit models. We then evaluated 4-bit and 2-bit students with and without the 6-bit teacher. As shown in Table 3, adding a 6-bit teacher generally improves performance, suggesting smoother supervision and enhanced knowledge transfer.\n\nHowever, the gains are not always substantial, and come at the cost of increased memory and training overhead. This demonstrates that our PMTD framework enables effective trade-offs between accuracy and resource consumption.\n\n#### Table 3: Impact of Intermediate-Bit Teachers on Student Performance and Training Overhead\n| Student | Teachers             | Peak Mem (GB) | PSNR (Vid4) | PSNR (Vimeo-Fast) | PSNR (Vimeo-Medium) | PSNR (Vimeo-Slow) |\n| ------- | -------------------- | ------------- | ----------- | ----------------- | ------------------- | ----------------- |\n| 4-bit   | FP32 + 8-bit         | 12.3          | 25.42       | 34.69             | 33.74               | 31.94             |\n| 4-bit   | FP32 + 8-bit + 6-bit | 16.3          | 25.46       | 34.69             | 33.75               | 31.94             |\n| 2-bit   | FP32 + 8-bit + 4-bit | 16.2          | 23.48       | 30.33             | 30.19               | 29.14             |\n| 2-bit   | FP32 + 8 + 6 + 4-bit | 20.5          | 23.48       | 30.36             | 30.21               | 29.13             |\n\n> **Q4:** Effectiveness of Percentile-Based Initialization in Suppressing Outliers and Optimal Range Selection\n\n**A4:** In BMFQ, we initialize the clipping bounds using fixed percentile ranges ([0.1–10] for lower bounds, [90–99.9] for upper bounds) instead of min-max. This helps suppress outliers by explicitly excluding the extreme tails of activation distributions, which are often noisy and unrepresentative. This design is motivated by empirical observation: after BTBI and PMTD optimization, most learned clipping points lie within this percentile range. We further validated this choice through an ablation study (Table 4), where our default setting consistently outperforms both wider and narrower ranges.\n\nIn summary, percentile-based initialization provides a robust starting point that avoids distortion from outliers while preserving meaningful dynamic range.\n#### Table 4: Impact of Percentile Initialization Ranges on Quantization Performance (4-bit)\n| Variant          | Range (lower/upper)      | PSNR (Vid4) | PSNR (Vimeo-Fast) | PSNR (Vimeo-Medium) | PSNR (Vimeo-Slow) |\n| ---------------- | ------------------------ | ----------- | ----------------- | ------------------- | ----------------- |\n| Default          | [0.1, 10] / [90, 99.9]   | **24.15**   | **32.24**         | **30.77**           | **30.66**         |\n| Wider Range ↑    | [0.01, 15] / [85, 99.99] | 23.99       | 32.01             | 29.86               | 30.24             |\n| Narrower Range ↓ | [0.1, 1] / [99, 99.9]    | 23.91       | 32.10             | 29.84               | 30.22             |"}]

Author rebuttal:

Meta-review: The paper received BA, A, BA, and A ratings. Overall, the reviewers have found some merit in this work.
Decision: Accept (poster)