Source-linked AI summary
Efficient Video Super-Resolution through Recurrent Latent Space Propagation
Dario Fuoli, Shuhang Gu, Radu Timofte
TL;DR
Video super-resolution needs to exploit temporal information while avoiding the accuracy and computational costs of explicit motion compensation. RLSP propagates temporal information through recurrent latent states, achieving state-of-the-art Vid4 accuracy while being more than 70× faster than DUF.
Problem
VSR must exploit temporal information, but challenging motion estimation can cause inaccurate compensation and computationally expensive processing.
Method
RLSP uses an end-to-end recurrent architecture with high-dimensional latent states to propagate temporal information implicitly without dedicated motion estimation.
Results
More than 70× faster than DUF, RLSP achieves the best accuracy on the Vid4 benchmark.
Takeaways & Limitations
RLSP combines efficient recurrent temporal propagation with state-of-the-art Vid4 accuracy, and its shallow, wide design supports parallel computation.
Takeaways & Limitations
RLSP initially produces lower PSNR because recurrent processing needs several frames to accumulate temporal information.
Abstract
from arXiv · showhide
With the recent trend for ultra high definition displays, the demand for high quality and efficient video super-resolution (VSR) has become more important than ever. Previous methods adopt complex motion compensation strategies to exploit temporal information when estimating the missing high frequency details. However, as the motion estimation problem is a highly challenging problem, inaccurate motion compensation may affect the performance of VSR algorithms. Furthermore, the complex motion compensation module may also introduce a heavy computational burden, which limits the application of these methods in real systems. In this paper, we propose an efficient recurrent latent space propagation (RLSP) algorithm for fast VSR. RLSP introduces high-dimensional latent states to propagate temporal information between frames in an implicit manner. Our experimental results show that RLSP is a highly efficient and effective method to deal with the VSR problem. We outperform current state-of-the-art method DUF with over 70x speed-up.
1. Introduction
VSR exploits temporal information from low-resolution video, but motion estimation and compensation can reduce accuracy and efficiency. RLSP addresses these challenges by propagating temporal information through recurrent latent states rather than explicit motion compensation.
- Temporal information is a key resource for improving video super-resolution from low-resolution observations.
- Most deep VSR methods estimate and compensate subpixel motion, but inaccurate alignment can produce blurred reconstructions and expensive computation limits real-time high-resolution processing.
- RLSP uses high-dimensional latent states in a recurrent architecture to propagate temporal information implicitly, avoiding explicit motion estimation and warping.
- RLSP achieves about 10× and 70× speed-up over FRVSR and DUF, respectively, while maintaining similar accuracy; RLSP 7-256 attains the highest PSNR on Vid4.
2. Related Work
Prior VSR methods combine multiple frames or recurrent processing, often using motion compensation to exploit temporal information. RLSP instead uses a recurrent hidden state to leverage temporal information implicitly without a dedicated motion-compensation module.
- Single-image super-resolution research progressed from shallow CNNs such as SRCNN to deeper residual networks such as VDSR.
- Many VSR methods combine adjacent low-resolution frames and use optical flow or warping before convolutional reconstruction.
- Recurrent VSR models can leverage information from potentially unlimited frames, unlike methods restricted to a fixed number of inputs.
- RLSP introduces a recurrent hidden state that implicitly leverages temporal information without a dedicated motion-compensation module.
3. Method
RLSP uses a recurrent, fully convolutional architecture to propagate temporal information through latent states while keeping most processing in LR space. Shuffling, residual connections, and recurrent inputs support efficient LR-to-HR reconstruction.
- 3. Method: RLSP maps LR video frames to HR outputs with a recurrent neural network that uses adjacent frames, previous outputs, and hidden states as inputs.The output is the brightness channel, while chroma channels are upscaled separately; models use r = 4.
- 3. Method: The network is fully convolutional and uses seven 3 × 3 convolution layers to prioritize efficiency and preserve local alignment.The architecture is not limited to a fixed input size and can accommodate video data of any dimensions.
- 3.1. Shuffling: Shuffling reversibly trades channel depth for spatial resolution, expanding both spatial dimensions by r while reducing channels by r^2.The transformation keeps processing in LR space until the final stage and reverses HR outputs before feedback.
- 3.1. Shuffling: For r = 4 and a single-channel HR output, the final LR tensor requires Z = 16 channels, implemented with 16 filters.The rearrangement preserves local integrity by mapping LR channel values into corresponding local HR regions.
- 3.2. Residual and recurrent inputs: A residual connection carries low-frequency information from the LR input directly toward the HR output, while feedback of the previous output supports temporal continuity.The previous HR output supplies already processed information and can reduce flickering between frames.
- 3.4. Hidden State: The hidden state propagates feature maps through concatenation as locality-based latent vectors in R^f, with spatial dimensions adjusted dynamically to the input size.Recurrence also lets each frame be processed twice before its estimate, increasing its effective receptive field without separate weights.
- 3.5. Loss: Training uses pixel-wise mean squared error between ground-truth frames and network outputs.The loss averages squared differences over the pixels in the ground-truth frames.
4. Experimental Setup
The experiments follow FRVSR’s dataset and preprocessing setup, training on available Vimeo videos and validating on additional YouTube videos. Training uses recurrent clips, Adam optimization, and batches of four.
- Dataset: The training data consists of 37 available high-resolution videos after three of the original 40 Vimeo videos became unavailable.The authors generate 40,000 random crops of size 20 × 256 × 256 × 3 as HR ground-truth sequences.
- Preprocessing: LR inputs are produced by applying Gaussian blur with σ = 1.5 and sampling every fourth pixel in both spatial dimensions.Ten additional high-resolution YouTube videos provide validation sequences generated with the same procedure.
- Training: The recurrent network is unrolled for 10 frames and trained by sampling 12 consecutive frames, with two extra frames supplying boundary inputs.Weights use Xavier initialization, batches contain four samples, and optimization uses Adam.
5. Results and Discussion
The experiments evaluate RLSP against established VSR methods using ablations, temporal-consistency analyses, information-flow tests, initialization behavior, and Vid4 accuracy–runtime comparisons. RLSP provides strong accuracy and temporal information propagation while substantially reducing runtime, although recurrent initialization causes weaker early-frame performance.
- 5.1. Ablation: RLSP ablations show that adjacent frames, recurrent feedback, and the locality-based hidden state progressively improve Vid4 PSNR.The complete RLSP 7-64 configuration gains 1.98dB over SISR while increasing runtime from 12ms by 2ms for feedback and another 5ms for the hidden state.
- 5.2. Temporal Consistency: Temporal profiles show sharper stripes for RLSP 7-128 than for DUF-52, indicating stronger temporal continuity in the reported visual comparison.DUF-52’s blurred vertical stripes indicate discontinuities between consecutive frames, while recurrent latent-state propagation provides greater temporal connectivity.
- 5.3. Information Flow over Time: RLSP 7-128 propagates information over almost 175 frames, with earlier accumulated information yielding up to 0.2dB higher PSNR for 150 frames.The two initialization runs eventually converge, but their separation indicates that the model retains information from a long temporal horizon.
- 5.5. Accuracy and Runtimes: RLSP 7-64 matches comparable PSNR from FRVSR 10-128 and DUF-16 while running 10× and 20× faster, respectively.
- 5.5. Accuracy and Runtimes: RLSP 7-256 improves PSNR by 0.65dB over FRVSR 10-128 and 0.21dB over DUF-52 while remaining over 2× and 30× faster, respectively.Increasing the filter count improves accuracy without proportionally increasing runtime in the reported comparison.
- 5.4. Initialization: RLSP 7-128 starts below DUF-52 in early frames but surpasses all other methods from frame 6 onward.The recurrent model reconstructs the full structure after frame 4, while DUF-52 still exhibits initialization artefacts in the cited sequence.
6. Conclusion
RLSP is an efficient recurrent VSR architecture that uses locality-based latent space propagation without dedicated motion estimation. It reduces runtime while maintaining state-of-the-art accuracy, and its accuracy can be improved by increasing model complexity.
- RLSP is an end-to-end trainable recurrent VSR architecture with locality-based latent space propagation and no dedicated motion estimation module.
- RLSP reduces runtime substantially while maintaining state-of-the-art accuracy by leveraging temporal information over long periods.
- RLSP achieves the best accuracy on Vid4 while running more than 70× faster than DUF.
- Increasing complexity, such as adding more filters, can further improve RLSP accuracy.