QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks

• URL: http://arxiv.org/abs/2605.22351v1
• Date: Thu, 21 May 2026 11:38:52 GMT
• Title: QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
• Abstract summary: Low-bit quantization is widely used to compress super-resolution (SR) models.<n>However, when SR models are pushed to ultra-low precision (2-4 bits), performance can drop sharply.<n>We propose QuantSR+, a unified framework that improves quantization operators, network design, and training optimization.
• Score: 63.04549067734431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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