Dynamic Resolution & FPS Sampling

• Dynamic Resolution and FPS Sampling is a multidisciplinary approach that adaptively manages both spatial and temporal sampling in imaging and signal processing.
• It leverages sensor-level innovations and non-regular sampling masks to optimize image acquisition while balancing resource constraints and scene complexity.
• Algorithmic strategies such as neural rendering, point cloud sampling, and space-time reconstruction enable high-fidelity performance in diverse real-time applications.

Dynamic resolution and FPS (frames per second) sampling is a multidisciplinary paradigm for jointly managing spatial and temporal sampling in signal acquisition, processing, and rendering. Contemporary research integrates principles from sensor design, computational imaging, neural rendering, point cloud geometry, and video processing to allocate sampling density adaptively in space and time according to scene complexity, resource constraints, or specific application demands. The objective is to maximize information capture and perception quality under physical, algorithmic, and system-level constraints. Rigorous quantitative analyses of trade-offs, optimal sampling schedules, and reconstruction procedures underpin state-of-the-art approaches.

1. Principles of Dynamic Resolution and FPS Sampling

Dynamic resolution refers to the adaptive allocation or recovery of spatial sampling density—either physically at the sensor/readout stage or computationally via post-acquisition processing. FPS sampling generalizes this adaptivity to temporal sampling, permitting the adjustment of frame rates, exposure intervals, or the effective number of processed frames, often under a fixed aggregate throughput (e.g., pixel-read rate, bandwidth, or computational budget).

The joint optimization of spatial resolution and temporal sampling aims to reconcile the classical trade-off: higher spatial fidelity is typically achieved at the cost of lower temporal coverage, and vice versa. The problem can be formulated as maximizing an application-specific quality metric subject to resource constraints:

$$\max_{S, R} Q(\text{spatial resolution } S, \text{frame rate } R) \quad \text{subject to} \quad C(S, R) \leq C_\text{max}$$

where $Q$ often exhibits sublinear (typically, logarithmic) growth in $S$ and $R$, while $C$ is typically proportional to their product (Li et al., 2024).

Applications span from high-speed X-ray radiography (Liu et al., 2022), video super-resolution (Li et al., 2024, Kim et al., 2019), dynamic sensor architectures (Jonscher et al., 2022), to adaptive neural rendering (He et al., 2023, Tan et al., 14 Apr 2025, Bai et al., 2023), and point cloud sampling (Li et al., 2022, Bhardwaj et al., 2024).

2. Sensor and Hardware Techniques for Spatio-Temporal Adaptivity

Sensor-level dynamic resolution and FPS sampling solutions leverage circuit or readout flexibility to modulate which pixels are sampled per frame and how frequently. One sensor architecture employs a CMOS array with dynamic charge-sensitive amplifier (CSA) tuning to achieve >300 fps and 5.5 lp/mm spatial resolution in X-ray imaging, where short integration windows and high readout bandwidths are constrained by electronics noise and data throughput (Liu et al., 2022). The CSA decay time and analog bandwidth are tuned to match per-pixel leakage currents, allowing dynamic adjustment of spatial and temporal response without excessive baseline drift or dynamic range loss.

Another class of approaches relies on dynamic non-regular sampling masks: at each frame, a unique subset of pixels is read (e.g., 25% coverage per frame with complementary patterns over four frames, guaranteeing complete coverage in aggregate) (Jonscher et al., 2022). This method enables increased effective frame rates (up to the reciprocal of the per-frame sampling density) at a fixed sensor throughput, with full spatial recovery achieved by computational 3D frequency-selective reconstruction (3D-FSR). The hardware is designed to facilitate fast permutation of readout patterns with minimal additional complexity. The realized benefit is in temporally interleaving measurement locations, thereby providing incoherent measurement gaps that can be algorithmically recovered if the signal admits joint spatio-temporal sparsity.

3. Algorithmic and Computational Approaches to Dynamic Sampling

Algorithmic strategies span from adaptive partitioned sampling (in geometry and rendering) to dynamic neural network branches and joint reconstruction methods.

In point cloud processing, adjustable farthest point sampling (AFPS) divides the point set into $M$ sectors, sampling $M$ farthest points per iteration, with resolution/speed control via $M$ (Li et al., 2022). Further, nearest-point-distance-updating (NPDU) restricts distance updates to spatially local windows, yielding $O(kC)$ total cost for typically constant $k$. Curvature-informed FPS (CFPS) combines geometry-aware metrics (curvature via learned MLP) with soft FPS coverage and a reinforcement learning block to dynamically swap sample points, producing non-uniform spatial resolution matched to salient features (e.g., edges, surface details) and yielding improved accuracy for downstream tasks (Bhardwaj et al., 2024).

For neural field rendering, dynamic-resolution ray- or voxel-sampling enables computational resources to be focused where high-frequency content or large modeling error signal their need. MCBlock applies Monte Carlo Tree Search to adaptively partition image space into blocks; coarse blocks in smooth regions and fine blocks in texturally complex or poorly fit regions (Tan et al., 14 Apr 2025). Dynamic PlenOctree (DOT) periodically splits octree voxels with large accumulated ray-weights and merges those with little contribution, refining sampling distributions as scene complexity evolves (Bai et al., 2023).

4. Joint Spatio-Temporal Reconstruction and Perceptual Trade-offs

A central problem is reconstructing full-resolution, high-FPS sequences from sub-Nyquist or non-uniform samples. Frequency-selective 3D methods (e.g., 3D-FSR) exploit sparsity in the 3D spatio-temporal spectrum to iteratively fill in missing (masked) voxels (Jonscher et al., 2022). Joint frame interpolation and super-resolution (VFI–SR) networks, such as FISR, employ multi-scale U-Net architectures with multi-scale temporal losses that encourage consistency across overlapping temporal windows and scales, producing spatially super-resolved and temporally interpolated frames (Kim et al., 2019). The network is constrained to match both pixel intensity and frame-to-frame difference over multiple windows, yielding coherent output across space and time.

Space-time supersampling (STSS) approaches for real-time rendering unify spatial upscaling and frame interpolation/extrapolation into a single neural operator, minimizing latency by taking advantage of shared low-level data (motion vectors, G-buffers) and handling both aliasing and warping holes via reshading modules (He et al., 2023).

A principled quality/cost framework is used to guide adaptive selection among resolution and FPS configurations. Perceptual models such as VSTR extract natural video statistics features that are sensitive to both spatial and temporal degradation, allowing predictive selection of optimal (resolution, framerate) pairs under bandwidth or computation constraints (Lee et al., 2021). For example, moderate temporal downsampling (e.g., halving FPS) combined with mild spatial downscaling often yields higher perceived quality than severe spatial downsampling alone.

5. Analytical Metrics and Performance Characterization

Quantitative analysis is central to the design and evaluation of dynamic resolution/FPS strategies. In imaging, the modulation transfer function (MTF) and detective quantum efficiency (DQE) characterize spatial fidelity and dose efficiency across frequencies (Liu et al., 2022). Noise power spectrum (NPS), signal-to-noise ratio (SNR), and LoD (detection limit) further inform system response. Dynamic sampling architectures must balance analog bandwidth, integration window, and data path throughput to maintain these metrics, with explicit formulas relating frame rate, pixel rate, and analog bandwidth:

• $T_\text{frame} = 1/\text{FPS}$
• $$\text{Pixel rate} = N_\text{pixels} \times \text{FPS}$$
• $B_\text{analog} \geq 1/(2 T_\text{pixel})$

In video systems and streaming, user-perceived quality is often modeled as $Q \approx \alpha \log_2 R + \beta \log_2 f$ (with $R$ the spatial resolution and $f$ the frame rate), while computational/resource cost scales as $C \approx \gamma Rf$. The optimal allocation sets $R/f = \alpha/\beta$, typically around 1.5:1 for SR workloads (Li et al., 2024). In video neural upscalers, practical constraints induce dynamic early-exit or branch merging/selection mechanisms to respect latency or memory budgets, with empirical PSNR trade-off curves measured as a function of resolution and FPS.

In scientific data-driven DMD, convergence properties are dissected via systematic parametric studies; “stabilization” in DMD is reached when modes converge (e.g., 11 cycles at ≥15 frames/cycle), with over-sampling ($m \rightarrow n$) causing divergence (Li et al., 2021).

6. Practical Implementations and Engineering Guidelines

Implementing dynamic resolution/FPS systems entails algorithmic, architectural, and system decisions:

• For hardware sensors: tune pixel-level amplification and gating for local leakage; maximize per-pixel charge collection at high frame rates via optimal μτ material selection; ensure readout channel can sustain aggregate throughput (Liu et al., 2022).
• In point cloud processing: pre-sort/bucket data along known dimensions, select $M$ based on trade-off, and deploy AFPS+NPDU for dynamic set abstraction (Li et al., 2022); integrate geometry priors and RL policies for further adaptivity (Bhardwaj et al., 2024).
• For neural field/radiance field training: deploy MCBlock or DOT to couple split/merge to texture/loss statistics, yielding acceleration factors of up to 2.33× in training (Tan et al., 14 Apr 2025, Bai et al., 2023).
• In neural upscaling/video streaming: shape-aware graph optimizations and content-aware patch splitting enable real-time, low-memory dynamic SR at target FPS budgets (Li et al., 2024); follow bandwidth/quality curves derived from perceptual models for runtime adaptation (Lee et al., 2021).
• Rendering: employ unified neural passes (e.g., STSS) for space-time super-resolution, with dynamic control of input resolution (α) and skip/extrapolation rate (β) to match latency and quality constraints (He et al., 2023).

A representative table from (Li et al., 2024) presents empirical PSNR across different upscaling factors and FPS:

Resolution	15 fps	30 fps	45 fps
×2	47.8	47.6	47.6
×3	44.9	44.7	44.6
×4	43.2	43.1	43.0

7. Applications, Limitations, and Future Directions

Dynamic resolution and FPS sampling enable real-time, high-fidelity imaging and visualization across domains: medical fluoroscopy and CT (Liu et al., 2022), high-throughput real-time rendering (He et al., 2023), telemedicine, AR/VR, and multi-GPU batch training in photorealistic neural radiance field synthesis (Tan et al., 14 Apr 2025). In networks, dynamic compiler-level shape inference and code fusion are crucial for supporting variable resolution/fps processing on-device (Li et al., 2024).

Notable limitations include increased algorithmic complexity in adaptive sampling policies, potential instability in fast-varying scenes (e.g., mask cycling confounding motion estimation in non-regular sampling (Jonscher et al., 2022)), and excessive resource/overhead in curvature-informed point cloud routines (Bhardwaj et al., 2024). In DMD, improper sampling schedules (e.g., over-sampling or under-resolving key modes) induce algorithmic divergence or bias (Li et al., 2021). Generalization and real-time hardware deployment require further exploration, particularly for fully dynamic, locally-adaptive architectures that couple hardware and neural policies.

Future inquiries focus on hierarchical multi-resolution sampling (Bhardwaj et al., 2024), explicit local adaptation to motion/content (Jonscher et al., 2022, He et al., 2023), integration with compression/encoding (Lee et al., 2021), and joint sensor-design with task-driven policies (Liu et al., 2022, Li et al., 2024). The synergy between dynamic adaptation, perceptual modeling, and real-time system constraints continues to drive advances in high-fidelity, resource-efficient imaging and signal processing.