Structured Sparsity Learning for Efficient Video Super-Resolution (2206.07687v3)

Abstract: The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, (e.g., smartphones and drones). Existing VSR models contain considerable redundant filters, which drag down the inference efficiency. To prune these unimportant filters, we develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of VSR. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks. Specifically, we develop a Residual Sparsity Connection (RSC) scheme for residual blocks of recurrent networks to liberate pruning restrictions and preserve the restoration information. For upsampling networks, we design a pixel-shuffle pruning scheme to guarantee the accuracy of feature channel-space conversion. In addition, we observe that pruning error would be amplified as the hidden states propagate along with recurrent networks. To alleviate the issue, we design Temporal Finetuning (TF). Extensive experiments show that SSL can significantly outperform recent methods quantitatively and qualitatively.

• The paper introduces a novel SSL framework that employs structured pruning to eliminate redundant filters in video super-resolution models.
• It integrates Residual Sparsity Connection and Pixel-Shuffle Pruning to maintain critical channel and spatial information during model optimization.
• Temporal Finetuning minimizes cumulative errors in recurrent networks, achieving superior performance with reduced computational load.

Structured Sparsity Learning for Efficient Video Super-Resolution

The computational demands of Video Super-Resolution (VSR) models significantly limit their deployment on devices with constrained resources, such as smartphones and drones. The research paper titled "Structured Sparsity Learning for Efficient Video Super-Resolution" introduces a novel framework known as Structured Sparsity Learning (SSL), a scheme designed to prune redundant filters in VSR models effectively while maintaining performance and enhancing efficiency.

Overview and Methodology

The paper identifies the core problem of redundancy in current VSR models, which leads to inefficiencies in computation and memory usage. The authors propose the SSL framework, which employs structured pruning techniques specifically tailored to leverage the properties unique to VSR tasks. The SSL framework is pioneering in its introduction of a structured pruning protocol that simultaneously addresses the repetitive nature of Conv layers in VSR models—an often-overlooked aspect in other pruning techniques which focus primarily on neural networks for image classification tasks.

Key Components of SSL:

1. Residual Sparsity Connection (RSC): The paper proposes the RSC component for efficient pruning of residual blocks within recurrent networks. This advancement overcomes traditional pruning constraints by preserving complete channel information, thus ensuring important restoration information is maintained. RSC enables greater pruning flexibility and efficiency compared to existing pruning schemes that often restrict pruning options at critical network layers.
2. Pixel-Shuffle Pruning Scheme: The pixel-shuffle operation is significant for upsampling in VSR, yet traditional pruning can disrupt spatial transformations. The SSL framework includes a specialized pruning mechanism that groups filters, ensuring transformation integrity post-pruning. This ensures that the channel-to-space conversion remains accurate, a critical aspect for VSR quality retention.
3. Temporal Finetuning (TF): As recurrent networks propagate hidden states, there is potential for cumulative pruning errors, which can diminish overall performance. TF addresses this by minimizing error accumulation through finetuning, thereby aligning pruned and unpruned network states.

Experimental Results

The empirical evaluation of the SSL framework presents robust quantitative and qualitative findings. SSL showed superior results over existing methods when applied to state-of-the-art VSR models like BasicVSR and its unidirectional variant BasicVSR-uni. This includes surpassing handcrafted VSR models like EDVR-M while reducing computational requirements by a significant margin. Notably, SSL achieves this while managing to uphold, if not improve, video restoration quality.

The paper provides comprehensive tables and visual comparisons that illustrate SSL's ability to outperform or closely match the performance of full-sized, non-pruned networks, even at aggressive pruning ratios. These results demonstrate SSL's substantial promise for enabling more efficient VSR model deployment on resource-limited devices without compromising on output quality.

Implications and Future Directions

The proposed SSL framework points to several implications and pathways for future research. Practically, SSL significantly enhances the deployability of sophisticated VSR models, opening avenues for their usage in real-world scenarios requiring real-time processing on edge devices. Theoretically, SSL sets a new benchmark in structured pruning methodologies specifically tailored for complex vision tasks beyond image classifications, such as VSR.

Future work can explore adaptive pruning strategies that further tailor SSL for model dynamism, perhaps learning from the data and context-specific requirements or integrating SSL with other model compression techniques like quantization or knowledge distillation. Moreover, as device capabilities continue to evolve, SSL provides a flexible basis for ongoing optimizations aligned with the advancing edge-computing landscape.

In conclusion, the Structured Sparsity Learning framework marks a significant contribution to both the field of VSR and the broader discipline of efficient neural network design. Through pioneering strategic pruning tailored for VSR, SSL stands to make high-quality video enhancement increasingly accessible and practical across diverse application domains.