Transparent Visual Prompt (TVP) in Video Super-Resolution

• Transparent Visual Prompt (TVP) is a learnable, zero-initialized image-level bias added to low-resolution frames to provide chunk-specific, data-dependent cues for super-resolution.
• It integrates with RepCaM++ by augmenting a re-parameterized super-resolution model without increasing inference-time architectural overhead.
• Empirical results show that TVP improves PSNR and preserves fine details with minimal transmission overhead (<0.1%), especially in long, multi-scene video sequences.

Searching arXiv for the specified paper and closely related work to ground the article in current literature. Transparent Visual Prompt (TVP) is a chunk-specific, zero-initialized, learnable image-level bias introduced in the neural video delivery framework RepCaM++, where it is overlaid onto each low-resolution video frame before super-resolution processing. In the formulation given for RepCaM++, the prompt is represented as $w_\phi\in\mathbb R^{S_H\times S_W\times C}$, is centered within the input frame, and is optimized jointly with the shared super-resolution backbone and Re-parameterization Content-aware Modulation (RepCaM) components using an $L_1$ reconstruction objective. TVP is designed for content-aware video super-resolution in long videos with multiple scene changes, where a single super-resolution model may lose overfitting fidelity to fine details; its role is to provide chunk-specific, data-dependent cues that emphasize subtle features such as edges and textures while imposing very small transmission overhead (Zhang et al., 17 Sep 2025).

1. Concept and role in neural video delivery

TVP is defined as a small learnable image-level bias term added directly to each low-resolution frame before the frame enters the super-resolution network. The defining property is its zero initialization: at initialization, the prompt is “transparent” in the sense that it does not alter the original pixel values. This preserves the integrity of the incoming frame while allowing the prompt to adapt during training to encode chunk-specific priors (Zhang et al., 17 Sep 2025).

Within RepCaM++, TVP addresses a limitation of prior content-aware delivery methods that customize super-resolution models for individual video chunks by introducing additional partial parameters. The description of RepCaM++ states that such approaches can accumulate parameters and may fail to adapt appropriately as video lengths increase, increasing delivery cost and reducing performance. TVP operates differently: rather than enlarging the deployed model at inference, it supplies a minimal external bias per chunk while the RepCaM branches are removed through inference-time re-parameterization. This division of labor suggests that TVP is intended to retain local chunk specialization without incurring the run-time architectural overhead associated with chunk-specific model branches (Zhang et al., 17 Sep 2025).

The stated motivation is especially tied to long videos with scene changes. Even when a shared model is modulated by RepCaM, fine-grained chunk-specific fidelity may degrade. TVP therefore serves as a lightweight mechanism for emphasizing “where to look” for subtle visual structures. In the supplied description, this is characterized as using hundreds of parameters and less than $0.1\%$ overhead to capture fine details within video chunks (Zhang et al., 17 Sep 2025).

2. Mathematical formulation

The low-resolution frame for a chunk is denoted $I^T\in\mathbb R^{H\times W\times C}$, and the TVP for that chunk is $w_\phi\in\mathbb R^{S_H\times S_W\times C}$. The prompt is inserted into the frame by centered placement with offsets

$$\Delta_H=\lfloor(H-S_H)/2\rfloor,\quad \Delta_W=\lfloor(W-S_W)/2\rfloor.$$

The prompt-augmented input is then defined as

$$I^T_{p}[i,j] = \begin{cases} I^T[i,j] + w_\phi[i-\Delta_H,\;j-\Delta_W], & (i,j)\in\mathcal R_{\text{TVP}},\ I^T[i,j], & \text{otherwise}, \end{cases}$$

where

$$\mathcal R_{\text{TVP}}=[\Delta_H,\Delta_H+S_H-1]\times[\Delta_W,\Delta_W+S_W-1].$$

This construction makes TVP an additive, localized perturbation of the input rather than a modification of model weights (Zhang et al., 17 Sep 2025).

The training loss is specified as

$\mathcal L = \tfrac1N\sum_{n=1}^N \bigl\|\,\mathrm{SR}(I^T_p^{(n)}) - I_{HR}^{(n)}\bigr\|_1.$

Because $I^T_p$ depends on $L_1$0 through simple addition, the gradient with respect to the prompt is given by

$L_1$1

with parameter update

$L_1$2

This makes the optimization dynamics straightforward: TVP is trained by backpropagation exactly through the image-space perturbation it induces. A plausible implication is that TVP can be integrated into existing super-resolution training pipelines with minimal algorithmic disruption, provided the input augmentation is differentiable, which it is in this formulation (Zhang et al., 17 Sep 2025).

3. Integration with RepCaM and the super-resolution backbone

RepCaM++ combines three components: a super-resolution backbone, RepCaM modules, and TVP. The backbone is exemplified by EDSR without batch normalization, while RepCaM augments that backbone using parallel-cascade $L_1$3 convolution branches. TVP is applied first to the raw input $L_1$4, producing $L_1$5, after which the augmented frame is processed by the shared SR+RepCaM model. All model weights, including RepCaM branch weights and the chunk-specific TVP, are jointly optimized (Zhang et al., 17 Sep 2025).

The chunk granularity is explicit. For each chunk of $L_1$6 low-resolution frames, a separate prompt $L_1$7 is learned. At inference, each $L_1$8 is delivered alongside its corresponding low-resolution chunk. The resulting architecture therefore separates static and dynamic elements: the backbone after re-parameterization is shared and fixed, while the prompt remains chunk-specific and external (Zhang et al., 17 Sep 2025).

This design is closely tied to inference-time re-parameterization. The RepCaM branches are folded into the original backbone through cascade conversion and branch concatenation, yielding a model with exactly the same architecture and parameter count as the original backbone at run time. TVP is not fused into the model; it remains an external image bias. This distinction is central to understanding TVP: unlike the RepCaM branches, it is not a training-only structure to be absorbed into convolution kernels, but a transmitted side input associated with each chunk (Zhang et al., 17 Sep 2025).

4. Training workflow

The training procedure described for RepCaM++ begins by splitting a long video into $L_1$9 contiguous chunks. The SR backbone and RepCaM extra branches are randomly initialized, while the TVP for each chunk is initialized to zero. During each training iteration, a batch of low-resolution and high-resolution patch pairs $0.1\%$0 is sampled from one chunk, and Video Patch Sampling (VPS) is applied to select informative regions in order to accelerate convergence (Zhang et al., 17 Sep 2025).

The prompt is then overlaid to compute $0.1\%$1, and the augmented input is propagated through the SR+RepCaM network to obtain $0.1\%$2. The training objective is the $0.1\%$3 loss $0.1\%$4. Backpropagation updates both the backbone and RepCaM weights using a standard learning rate and decay, and the TVP $0.1\%$5 using the same learning rate. The process is repeated across all chunks, with chunk-specific TVPs learned in parallel within a single end-to-end run (Zhang et al., 17 Sep 2025).

This training scheme makes TVP a prompt in the literal sense of an optimized input-side conditioning signal. However, it differs from textual or token-based prompting paradigms common in multimodal transformers. Here the prompt is an image-level additive bias localized to a spatial region of the frame. A plausible implication is that the “visual prompt” terminology in this context refers to spatial conditioning in pixel space rather than semantic instruction or language-aligned control.

5. Inference, transmission model, and efficiency

At inference, RepCaM++ relies on re-parameterization to eliminate the run-time cost of RepCaM branches. The two stated steps are cascade conversion, which merges $0.1\%$6 chains into one $0.1\%$7 convolution, and branch concatenation, which merges multi-branch kernels into a single kernel. After this process, the deployed super-resolution network has the same architecture and parameter count as the original backbone, so there is no RepCaM overhead at run time (Zhang et al., 17 Sep 2025).

TVP remains external and is transmitted per chunk. The description gives a transmission model with model size $0.1\%$8, low-resolution chunk size $0.1\%$9, and TVP size $I^T\in\mathbb R^{H\times W\times C}$0, yielding total transmission

$I^T\in\mathbb R^{H\times W\times C}$1

For the EDSR-based configuration, the model size is given as $I^T\in\mathbb R^{H\times W\times C}$2. An example TVP size is $I^T\in\mathbb R^{H\times W\times C}$3 for nine $I^T\in\mathbb R^{H\times W\times C}$4 prompts over a $I^T\in\mathbb R^{H\times W\times C}$5 video, described as under $I^T\in\mathbb R^{H\times W\times C}$6 extra overhead. The point of this accounting is that TVP contributes a small per-chunk payload while avoiding growth in deployed model size (Zhang et al., 17 Sep 2025).

The run-time result after re-parameterization is reported as real-time operation at $I^T\in\mathbb R^{H\times W\times C}$7 FPS on Snapdragon 888 with no extra cost beyond the original super-resolution model. Because TVP is applied as an additive image bias and is not fused into the network, its computational role at inference is separate from the structural simplification obtained through RepCaM re-parameterization. This suggests a two-part deployment strategy: static network compression through kernel folding, coupled with dynamic per-chunk conditioning through small transmitted prompts (Zhang et al., 17 Sep 2025).

6. Empirical performance and observed effects

The reported experiments are conducted on the VSD4K dataset across six different video scenes. For $I^T\in\mathbb R^{H\times W\times C}$8 videos at $I^T\in\mathbb R^{H\times W\times C}$9 upscaling with an EDSR backbone, the comparison includes a baseline CaFM result of $w_\phi\in\mathbb R^{S_H\times S_W\times C}$0 PSNR, EMT at $w_\phi\in\mathbb R^{S_H\times S_W\times C}$1, and RepCaM++ with TVP at $w_\phi\in\mathbb R^{S_H\times S_W\times C}$2, corresponding to a $w_\phi\in\mathbb R^{S_H\times S_W\times C}$3 gain relative to EMT as stated in the supplied data (Zhang et al., 17 Sep 2025).

Additional image-quality metrics are reported for city45: LPIPS decreases from approximately $w_\phi\in\mathbb R^{S_H\times S_W\times C}$4 to $w_\phi\in\mathbb R^{S_H\times S_W\times C}$5, while SSIM rises from approximately $w_\phi\in\mathbb R^{S_H\times S_W\times C}$6 to $w_\phi\in\mathbb R^{S_H\times S_W\times C}$7. On longer sequences from $w_\phi\in\mathbb R^{S_H\times S_W\times C}$8 to $w_\phi\in\mathbb R^{S_H\times S_W\times C}$9, the reported PSNR gains are $$\Delta_H=\lfloor(H-S_H)/2\rfloor,\quad \Delta_W=\lfloor(W-S_W)/2\rfloor.$$0–$$\Delta_H=\lfloor(H-S_H)/2\rfloor,\quad \Delta_W=\lfloor(W-S_W)/2\rfloor.$$1 versus state-of-the-art across all scales. Compared with H.264 and H.265 at equal bitrate, RepCaM++ with TVP yields $$\Delta_H=\lfloor(H-S_H)/2\rfloor,\quad \Delta_W=\lfloor(W-S_W)/2\rfloor.$$2–$$\Delta_H=\lfloor(H-S_H)/2\rfloor,\quad \Delta_W=\lfloor(W-S_W)/2\rfloor.$$3 higher PSNR across six VSD4K scenes (Zhang et al., 17 Sep 2025).

The qualitative examples emphasize recovery of fine details: small text and numbers in game and vlog content become legible, skateboard patterns in sport scenes become crisp, and fine jewelry contours in interview footage reappear. These examples align with the stated motivation that TVP captures fine details and directs the shared model toward informative regions. A plausible implication is that the most visible gains arise in regions whose reconstruction is underdetermined by low-resolution evidence alone and therefore benefits from chunk-specific priors (Zhang et al., 17 Sep 2025).

Setting	Reported result	Context
CaFM	33.12 dB PSNR	45 s videos, $$\Delta_H=\lfloor(H-S_H)/2\rfloor,\quad \Delta_W=\lfloor(W-S_W)/2\rfloor.$$4, EDSR backbone
EMT	33.36 dB PSNR	45 s videos, $$\Delta_H=\lfloor(H-S_H)/2\rfloor,\quad \Delta_W=\lfloor(W-S_W)/2\rfloor.$$5, EDSR backbone
RepCaM++ with TVP	33.74 dB PSNR	45 s videos, $$\Delta_H=\lfloor(H-S_H)/2\rfloor,\quad \Delta_W=\lfloor(W-S_W)/2\rfloor.$$6, EDSR backbone

7. Interpretation, scope, and potential misconceptions

A common misconception would be to treat TVP as a permanent enlargement of the super-resolution model. In the RepCaM++ description, this is not the case. The additional RepCaM branches are removed through re-parameterization, and TVP is not fused into the model; instead it is a separately transmitted chunk-level image bias. The deployed model therefore retains the original backbone architecture and parameter count, while chunk-specific specialization is carried by the prompt (Zhang et al., 17 Sep 2025).

Another possible misconception is that “transparent” means the prompt remains visually or numerically negligible throughout training. The supplied definition is narrower: transparency refers to zero initialization, ensuring that the prompt does not corrupt original pixel values at initialization. During optimization, the prompt is free to adapt and encode chunk-specific priors. Thus, transparency is an initialization property rather than a constraint on the learned values (Zhang et al., 17 Sep 2025).

TVP also should not be conflated with generic scene-level metadata or explicit saliency maps. The formulation given is a learned tensor added directly to a centered spatial region of the input frame. Its effect is mediated entirely through end-to-end optimization under the super-resolution loss, without a separate supervisory signal for saliency or region selection. This suggests that TVP functions as an implicit chunk-conditioned prior embedded in pixel space rather than as an interpretable annotation layer.

More broadly, TVP occupies a specific position within content-aware neural video delivery: it complements a shared re-parameterized model by restoring chunk-level adaptability with negligible overhead. In the formulation reported for RepCaM++, this combination enables state-of-the-art video restoration quality and delivery bandwidth compression on the evaluated scenarios, while preserving real-time deployment characteristics after inference-time re-parameterization (Zhang et al., 17 Sep 2025).