Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution

Jinwoo Chung; Jangho Kim

cs.AIOct 12, 2026

Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution

Authors: Jinwoo Chung, Jangho Kim

Organizations: Kookmin University Seoul, Republic of Korea

Abstract

Mamba-based super-resolution models are efficient alternatives to Transformer-based restoration backbones, but their low-bit post-training quantization remains challenging because selective scan, Mamba's input-dependent state-space operator, induces different activation ranges across spatial tokens. Thus, a single layer-wise clipping range poorly matches all tokens. We propose Token-Group Quantization, a hardware-friendly activation PTQ framework that statically approximates a hardware-unfriendly post-scan grouping oracle, where tokens with similar preferred clipping ranges are assigned to the same group. To avoid runtime post-scan grouping in static low-bit inference, a lightweight predictor infers the groups from pre-scan features available before the quantized scan path. During calibration, we form pseudo-labels by clustering post-scan activation descriptors that capture similar rounding--clipping behavior. At inference, the frozen predictor maps pre-scan features to groups and selects entries from offline-calibrated group-wise clipping-bound tables, without runtime statistics collection, dynamic clipping updates, or post-scan group assignment. To address low-bit restoration degradation, we further introduce a Frequency-Preserving Refinement (FPR) objective for clipping-bound calibration. FPR aligns Fourier amplitude and phase between full-precision teacher and quantized student outputs to preserve high-frequency structures. Across PTQ protocols, bit-widths, backbones, and tasks, our method consistently outperforms prior Mamba and SR PTQ baselines, transfers to Transformer SR and Vision Mamba classification, composes with rotation-based quantization, and achieves 2.23x speedup on Jetson Orin Nano over FP16. Code is available here.

Figures & tables

Panel of Figure 1 — Figure 1: Selective scan induces token-wise activation heterogeneity. (a) Post-scan activations exhibit a broader distribution than pre-scan activations in a representative ASSM layer (layer 18). (b) Per-token range CoV increases after selective scan in most ASSM layers.

Figure 2: Clipping-scale regimes. Violin plots show token-level preferred clipping scales $c_{i}^{\star}$ under random, post-scan descriptor K-means (oracle), and predicted groups.

Figure 3: Overview of Token-Group Quantization. A frozen pre-scan predictor predicts token groups, and grouped INT sites use offline-calibrated clipping-bound tables without runtime statistics.

Scale	Method	W / A	Set5		Set14		BSD100		Urban100		Manga109
Scale	Method	W / A	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
$\times$ 4	MambaIRv2-Light	32 / 32	32.51	0.8992	28.84	0.7878	27.75	0.7426	26.82	0.8079	31.24	0.9182
	PTQ4VM [ 8 ] (WACV’25)	4 / 4	30.82	0.8670	27.69	0.7546	26.95	0.7115	24.76	0.7321	28.19	0.8660
	Quamba [ 6 ] (ICLR’25)	4 / 4	31.01	0.8715	27.77	0.7585	26.99	0.7149	25.01	0.7470	28.57	0.8752
	MambaQuant [ 44 ] (ICLR’25)	4 / 4	30.74	0.8650	27.17	0.7413	26.37	0.6920	23.28	0.6694	26.73	0.8186
	OAQ-SR [ 38 ] (ICCV’25)	4 / 4	30.40	0.8577	27.41	0.7470	26.75	0.7045	24.31	0.7136	27.39	0.8485

Table 1: Quantitative comparison under the Weight-freeze PTQ protocol on MambaIRv2-Light.

Module	FLOPs	Ratio	Quant.
	(G)	(%)
Linear + Conv	8.49	85.9	Yes
Scan operands	0.49	4.9	Yes
Other Conv	0.90	9.1	No
Total	9.88	100.0	–

Table 2: FLOPs and W/A quantization scope.

Scale	Method	W / A	Set5		Set14		BSD100		Urban100		Manga109
Scale	Method	W / A	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
$\times$ 4	MambaIRv2-Light	32 / 32	32.51	0.8992	28.84	0.7878	27.75	0.7426	26.82	0.8079	31.24	0.9182
	AIQViT [ 21 ] (AAAI’25)	4 / 4	31.51	0.8856	28.10	0.7707	27.26	0.7268	25.18	0.7546	28.76	0.8854
	SPR 2 Q [ 43 ] (ICLR’26)	4 / 4	32.04	0.8929	28.59	0.7812	27.52	0.7359	26.07	0.7864	30.12	0.9048
	Ours + LoRA	4 / 4	32.25	0.8953	28.67	0.7830	27.61	0.7377	26.24	0.7906	30.49	0.9095
	AIQViT [ 21 ] (AAAI’25)	2 / 2	30.52	0.8667	27.44	0.7541	26.79	0.7102	24.35	0.7210	27.27	0.8515

Table 3: Quantitative comparison under the Weight-training PTQ with LoRA protocol on MambaIRv2-Light.

Figure 4: Visual comparison for image SR ( $\times 2$ and $\times 4$ ) on Urban100 under 4-bit PTQ.

Table 8

Table 9

Appendix figures & tables15 assets

Supplementary material from the paper’s appendix.

Appendix

Scale	Method	W / A	Set5		Set14		BSD100		Urban100		Manga109
Scale	Method	W / A	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
$\times$ 4	MambaIRv1-Light	32 / 32	32.42	0.8977	28.74	0.7847	27.68	0.7400	26.52	0.7983	30.94	0.9135
	Layer-wise PTQ	4 / 4	30.39	0.8257	27.45	0.7249	26.71	0.6811	24.99	0.7185	28.62	0.8372
	OAQ-SR [ 38 ] (ICCV’25)	4 / 4	30.85	0.8507	27.80	0.7446	26.93	0.7019	25.03	0.7392	28.59	0.8611
	Ours	4 / 4	31.10	0.8627	27.91	0.7534	27.09	0.7097	25.23	0.7444	28.90	0.8682
	Layer-wise PTQ	2 / 2	23.10	0.4199	22.29	0.3725	22.25	0.3453	20.92	0.3736	21.99	0.4017

Appendix

Table 9: Generalization results on MambaIRv1-Light under the Weight-freeze PTQ protocol.

Method	W / A	CBSD68		Kodak24		McMaster		Urban100
Method	W / A	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
MambaIRv2-Base	32 / 32	34.15	0.9388	35.15	0.9363	35.07	0.9421	34.72	0.9484
Layer-wise PTQ	4 / 4	32.84	0.9161	33.49	0.9098	33.06	0.9169	32.62	0.9229
OAQ-SR [ 38 ] (ICCV’25)	4 / 4	33.52	0.9290	34.33	0.9243	34.04	0.9305	33.59	0.9371
Ours	4 / 4	33.78	0.9335	34.69	0.9299	34.51	0.9356	34.04	0.9419
Layer-wise PTQ	2 / 2	27.20	0.7871	27.00	0.7667	24.31	0.7564	25.44	0.7860

Appendix

Table 10: Generalization results on color image denoising with noise level

\sigma=15

under the Weight-freeze PTQ protocol.

Quality	Method	W / A	Classic5		LIVE1
Quality	Method	W / A	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
$q=10$	MambaIRv2-Base	32 / 32	30.37	0.8269	29.91	0.8301
	Layer-wise PTQ	4 / 4	28.95	0.7834	28.74	0.7928
	OAQ-SR [ 38 ] (ICCV’25)	4 / 4	29.59	0.8064	29.26	0.8125
	Ours	4 / 4	29.93	0.8183	29.57	0.8230
	Layer-wise PTQ	2 / 2	27.57	0.7324	27.48	0.7474

Appendix

Table 11: Generalization results on JPEG artifact removal under the Weight-freeze PTQ protocol.

Method	W / A	Set5		Set14		BSD100		Urban100		Manga109
Method	W / A	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
SwinIR-light	32 / 32	38.14	0.9611	33.86	0.9206	32.31	0.9012	32.76	0.9340	39.12	0.9783
2DQuant (NeurIPS’24)	4 / 4	37.87	0.9594	33.41	0.9161	32.02	0.8971	31.84	0.9251	38.31	0.9761
FIMA-Q (CVPR’25)	4 / 4	37.71	0.9598	33.38	0.9160	32.01	0.8964	31.95	0.9283	38.44	0.9764
APHQ-ViT (CVPR’25)	4 / 4	37.76	0.9591	33.41	0.9165	31.94	0.8965	31.98	0.9280	38.34	0.9759
Ours	4 / 4	37.91	0.9600	33.47	0.9171	32.09	0.8978	32.06	0.9287	38.51	0.9773

Appendix

Table 12:

\times 2

SR on SwinIR-light under the Weight-freeze PTQ protocol.

Method	W / A	Set5		Set14		BSD100		Urban100		Manga109
Method	W / A	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
SwinIR-light	32 / 32	38.14	0.9611	33.86	0.9206	32.31	0.9012	32.76	0.9340	39.12	0.9783
SPR 2 Q (ICLR’26)	4 / 4	37.95	0.9602	33.55	0.9178	32.14	0.8989	32.04	0.9275	38.59	0.9769
Ours + LoRA	4 / 4	38.04	0.9608	33.65	0.9193	32.22	0.9003	32.28	0.9302	38.75	0.9776
SPR 2 Q (ICLR’26)	2 / 2	37.28	0.9572	32.83	0.9113	31.63	0.8921	30.20	0.9077	37.08	0.9726
Ours + LoRA	2 / 2	37.69	0.9596	33.22	0.9157	31.96	0.8971	31.16	0.9193	37.74	0.9753

Appendix

Table 13:

\times 2

SR on SwinIR-light under the Weight-training PTQ with LoRA protocol.

Method	W / A	Vim-Tiny	Vim-Small
FP	32 / 32	76.07	80.48
Layer-wise PTQ	6 / 6	71.92	76.66
PTQ4VM (WACV’25)	6 / 6	74.42	79.62
Ours	6 / 6	75.88	79.95
Layer-wise PTQ	4 / 4	53.61	67.06
PTQ4VM (WACV’25)	4 / 4	56.62	70.41

Appendix

Table 14: ImageNet Top-1 accuracy in percent on Vision Mamba under PTQ. Calibration and quantization scope follow PTQ4VM.

Backbone	Method	W / A	Set14		Urban100
Backbone	Method	W / A	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
MambaIRv2-Base	FP	32 / 32	34.89	0.9275	34.49	0.9468
	Layer-wise PTQ	4 / 4	32.39	0.9060	29.25	0.8930
	OAQ-SR (ICCV’25)	4 / 4	33.04	0.9132	31.25	0.9195
	Ours	4 / 4	33.28	0.9157	31.60	0.9236
	Layer-wise PTQ	2 / 2	30.54	0.8556	27.53	0.8398

Appendix

Table 15:

\times 2

SR on MambaIRv1-Base and MambaIRv2-Base under the Weight-freeze PTQ protocol.

Method	W / A	Set5		Set14		BSD100		Urban100		Manga109
Method	W / A	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
MambaIRv2-Light	32 / 32	38.26	0.9615	34.09	0.9221	32.36	0.9019	33.26	0.9378	39.35	0.9785
Affine	4 / 4	35.80	0.9452	32.81	0.9091	30.89	0.8789	30.57	0.9163	34.67	0.9550
Hadamard	4 / 4	36.33	0.9492	32.61	0.9085	31.04	0.8823	30.36	0.9123	35.73	0.9655
SpinQuant	4 / 4	36.42	0.9500	32.50	0.9063	31.08	0.8828	30.74	0.9188	35.54	0.9622
Ours	4 / 4	37.56	0.9578	33.24	0.9149	31.86	0.8950	31.34	0.9204	37.85	0.9742

Appendix

Table 16:

\times 2

SR on MambaIRv2-Light with channel-axis transforms ported onto the identical Weight-freeze PTQ protocol. Affine covers AffineQuant and FlatQuant, and Hadamard covers QuaRot.

Perturbation rate $\rho$	Set14		Urban100
Perturbation rate $\rho$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
$\rho=0\%$ (ours, no perturbation)	33.24	0.9149	31.34	0.9204
$\rho=5\%$	33.21	0.9145	31.34	0.9204
$\rho=10\%$	33.18	0.9143	31.33	0.9202
$\rho=20\%$	33.09	0.9137	31.25	0.9193
$\rho=30\%$	33.01	0.9127	31.13	0.9178

Appendix

Table 17: Robustness to inference-time predictor perturbation on MambaIRv2-Light (

\times 2

, W4A4) under the Weight-freeze PTQ protocol. We use the calibrated bound tables and the trained predictor from our default setting and, only at evaluation time, randomly reassign a fraction

\rho

of predicted group ids to a uniformly random group in

\{1,\dots,K\}

. Calibration, bound refinement, and the predictor are unchanged across rows. The

\rho{=}0\%

row matches our default deployment.

LR crop	Tokens	Ratio to calib.	FP		Ours		Gap
LR crop	Tokens	Ratio to calib.	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
$32\times 32$	1,024	0.25 $\times$	33.77	0.9127	31.88	0.8929	1.89	0.0198
$64\times 64$	4,096	1 $\times$	32.60	0.9084	30.48	0.8825	2.12	0.0259
$128\times 128$	16,384	4 $\times$	31.89	0.9105	29.78	0.8873	2.11	0.0232
$256\times 256$	65,536	16 $\times$	32.13	0.9217	30.07	0.9003	2.06	0.0214

Appendix

Table 18: Resolution robustness on Urban100 at

\times 2

and W4A4 over 20 images. Predictor and bound tables are frozen and only the LR input crop size varies. Gap is FP minus Ours.

$K$	Set14		Urban100
$K$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
1	32.55	0.9099	30.98	0.9084
4	33.20	0.9141	31.37	0.9193
8, default	33.28	0.9157	31.60	0.9236
12	33.34	0.9163	31.68	0.9244
16	33.37	0.9168	31.71	0.9249

Appendix

Table 19: Effect of the number of token groups

K

on MambaIRv2-Base at

\times 2

and W4A4.

Frequency term	Amplitude error			Phase error
Frequency term	Low	Mid	High	Low	Mid	High
none	0.0208	0.2853	0.536	0.0213	0.3211	0.673
+ amplitude only	0.0206	0.2784	0.512	0.0212	0.3191	0.668
+ phase only	0.0213	0.2830	0.531	0.0216	0.3181	0.635
+ $\mathcal{L}_{\mathrm{FPR}}$ , Ours	0.0214	0.2850	0.518	0.0219	0.3213	0.643

Appendix

Table 20: Band-wise Fourier errors of the quantized output against the FP output on Urban100 at

\times 2

and W4A4. Lower is better. All variants use token groups, and none denotes

\mathcal{L}_{\mathrm{pix}}{+}\mathcal{L}_{\mathrm{feat}}

without any frequency term.

$\lambda_{\mathrm{FPR}}$	Set14		Urban100
$\lambda_{\mathrm{FPR}}$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
$0.005$	33.11	0.9124	31.19	0.9184
$0.01$ (ours)	33.24	0.9149	31.34	0.9204
$0.05$	33.22	0.9140	31.31	0.9197
$0.1$	33.09	0.9116	31.04	0.9175

Appendix

Table 21: Sensitivity to FPR objective weight

\lambda_{\mathrm{FPR}}

on MambaIRv2-Light (

\times 2

, W4A4) under the Weight-freeze PTQ protocol. All other settings are kept at their default values.

\lambda_{\mathrm{FPR}}{=}0.01

corresponds to our default setting.

Figure 5: Clipping-scale regimes in early/middle layers. Token-level preferred clipping scales

c_{i}^{\star}

for layers 1 and 6 under random, post-scan descriptor K-means (oracle), and predicted groups.

Figure 6: Clipping-scale regimes in later layers. Token-level preferred clipping scales

c_{i}^{\star}

for layers 11 and 16 under random, post-scan descriptor K-means (oracle), and predicted groups.

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Low-bit quantization is widely used to compress super-resolution (SR) models and reduce storage and computation costs for deployment on resource-limited devices. However, when SR models are pushed to ultra-low precision (2-4 bits), performance can drop sharply due to diminished representational capacity and the detail-sensitive nature of SR. To address these issues, we propose QuantSR+, a unified framework that improves quantization operators, network design, and training optimization, achieving better trade-offs between accuracy and efficiency than prior low-bit SR methods. QuantSR+ mainly relies on three technical contributions: (1) Redistribution-driven Bit Determination (RBD), which reshapes quantization distributions in both forward and backward passes to preserve representation fidelity; (2) Quantized Slimmable Architecture (QSA), which begins with an over-parameterized model and progressively prunes less critical blocks to meet efficiency budgets while pushing the accuracy performance; and (3) Slimming-guided Function-localized Distillation (SFD), which enforces block-aware feature alignment via a direct loss and a progressive, function-local training schedule to capture quantization effects better and speed up convergence. Extensive experiments show that QuantSR+ achieves state-of-the-art performance against both specialized quantized SR methods and generic quantization approaches. For SwinIR-S on Urban100 (x4), it improves PSNR by 0.29 dB over the 2-bit SOTA baseline. Meanwhile, it delivers strong efficiency gains at 2-bit, reducing operations by up to 87.9% and storage by 89.4%. QuantSR+ is effective for both convolutional and transformer-based SR models, indicating broad applicability.

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