Efficient Continuous Image Representation through Latent Modulated Function

核心概念

The proposed Latent Modulated Function (LMF) decouples the high-resolution high-dimensional decoding process into shared latent decoding and independent rendering, realizing a computational optimal paradigm for continuous image representation.

要約

The content presents a novel Latent Modulated Function (LMF) for efficient continuous image representation.
Key highlights:

Existing Implicit Neural Representation (INR)-based Arbitrary-Scale Super-Resolution (ASSR) methods adhere to a decoding paradigm in high-resolution high-dimensional (HR-HD) space, leading to prohibitively high computational cost and runtime.
LMF decouples the HR-HD decoding process into shared latent decoding in low-resolution high-dimensional (LR-HD) space and independent rendering in high-resolution low-dimensional (HR-LD) space, realizing a computational optimal paradigm.
LMF utilizes a latent MLP to generate latent modulations, which are then applied to adjust the hidden layers of a lightweight render MLP for efficient arbitrary-resolution rendering.
Based on the positive correlation between modulation intensity and input image complexity, a Controllable Multi-Scale Rendering (CMSR) algorithm is proposed to balance rendering efficiency and precision.
Extensive experiments demonstrate that converting existing INR-based ASSR methods to LMF can reduce computational cost by up to 99.9%, accelerate inference by up to 57×, and save up to 76% of parameters, while maintaining competitive performance.

統計

The computational cost (MACs) of LMF-based ASSR methods is reduced by 90.4% to 99.9% compared to the original INR-based methods.
The inference runtime of LMF-based ASSR methods is accelerated by 2.4× to 56.9× compared to the original INR-based methods.
The number of parameters in LMF-based ASSR methods is reduced by 45.1% to 76.0% compared to the original INR-based methods.

引用

"LMF successfully disentangles the decoding process from HR-HD space into latent (LR-HD) and render (HR-LD) spaces."
"Based on the positive correlation between modulation intensity and input image complexity, we propose a Controllable Multi-Scale Rendering (CMSR) algorithm to effortlessly balance the rendering efficiency and precision at test time."

抽出されたキーインサイト

Latent Modulated Function for Computational Optimal Continuous Image Representation

by Zongyao He,Z... 場所 arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16451.pdf

深掘り質問

How can the proposed LMF paradigm be extended to other continuous signal representation tasks beyond image processing

The proposed Latent Modulated Function (LMF) paradigm can be extended to other continuous signal representation tasks beyond image processing by adapting the concept of latent modulation to different types of data and signals. For example, in audio processing, the latent modulation approach can be applied to tasks such as speech recognition, music generation, and sound synthesis. By using a latent MLP to generate modulations for the features extracted from audio signals, a render MLP can then efficiently decode these modulated features to produce high-quality audio outputs. Similarly, in natural language processing, the latent modulation technique can be utilized for tasks like language translation, sentiment analysis, and text generation. By incorporating latent modulations into the decoding process, the model can effectively represent and generate continuous text data. Overall, the LMF paradigm can be adapted and extended to various continuous signal representation tasks by customizing the latent and render MLPs to suit the specific characteristics of the data domain.

What are the potential limitations or drawbacks of the latent modulation approach, and how can they be addressed in future research

One potential limitation of the latent modulation approach is the need for careful design and tuning of the modulation parameters. The effectiveness of latent modulation relies on capturing the signal complexity of the input data accurately. If the modulation intensity is not appropriately adjusted based on the input data characteristics, it may lead to suboptimal performance and reduced quality of the output. To address this limitation, future research can focus on developing adaptive modulation schemes that dynamically adjust the modulation parameters based on the input data distribution. Additionally, exploring different modulation architectures and techniques, such as attention mechanisms or recurrent neural networks, can enhance the flexibility and robustness of the latent modulation approach. By incorporating more sophisticated modulation strategies, researchers can mitigate the limitations of the latent modulation approach and improve its performance across a wide range of signal representation tasks.

Given the significant efficiency improvements, how can the LMF-based continuous image representation be leveraged to enable new real-world applications that were previously infeasible

The significant efficiency improvements offered by the LMF-based continuous image representation open up new possibilities for real-world applications that were previously infeasible due to computational constraints. One key application area that can benefit from the efficiency of LMF is real-time video processing and streaming. By leveraging the computational optimal paradigm of LMF, video processing tasks such as video super-resolution, object detection, and video compression can be performed with reduced computational cost and faster inference times. This can lead to improved video quality, reduced latency, and enhanced user experience in applications like video conferencing, surveillance systems, and video streaming platforms. Additionally, the efficiency gains of LMF can enable the deployment of high-quality image processing algorithms on resource-constrained devices such as mobile phones, IoT devices, and edge computing platforms. This can facilitate the integration of advanced image processing capabilities into a wide range of applications, including mobile photography, augmented reality, and smart home devices. Overall, the LMF-based continuous image representation has the potential to revolutionize the efficiency and scalability of image processing applications, paving the way for innovative solutions in various real-world scenarios.