This paper presents a study on the computational complexity of coding for machines, with a focus on image coding for classification. We first conduct a comprehensive set of experiments to analyze the size of the encoder (which encodes images to ...

The way to monitor the safety of infrastructure facilities such as power towers by human beings on the ground faces great risks under extreme environmental and climatic conditions. Therefore, automatic, real-time and long-term monitoring of power ...$$

Recently, learned image compression (LIC) has shown significant research potential. Most existing LIC methods are CNN-based or transformer-based or mixed. However, these LIC methods suffer from a certain degree of degradation in global attention ...

In recent years, learned image compression has witnessed significant advancements. However, many existing learned image compression models predominantly rely on Convolutional Neural Networks (CNNs) and predicting the distribution of latent ...

• Achieving a balance between file size reduction and image quality preservation.
• Neighborhood-based Attention is more suitable for image compression.
• Improving entropy model by multi-scale guiding.
• Accurate feature capture ...

In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time resulting from the serial autoregressive context model, the parallel ...

• Investigating Performance Degradation in Existing Parallel Context Models: Exploring Quantity and Quality Perspectives.
• Introducing a Novel Transformer-Based Context Model with Corner-to-Center Processing, Logarithmic-Based Prediction ...

In the field of image processing and computer vision (CV), machine learning (ML) architectures are widely used. Image compression problems can be solved using convolutional neural networks (CNNs). As a result of bandwidth and memory constraints, ...

• Discussion about the background of image compression.
• Classification of image compression frameworks into subgroups based on architectures.
• Insight about open research problems and future research directions in learning-driven ...

Efficient image compression is very important for storage, retrieval, processing and transmission of image contents. The objective is to find a striking balance between compression ratio and the distortion in image. Recently, there has been a rise ...

In Cloud 3D, such as Cloud Gaming and Cloud Virtual Reality (VR), image frames are rendered and compressed (encoded) in the cloud, and sent to the clients for users to view. For low latency and high image quality, fast, high compression rate, and ...

Entropy modeling is a key component for high-performance image compression algorithms. Recent developments in autoregressive context modeling helped learning-based methods to surpass their classical counterparts. However, the performance of those ...

Deep neural networks (DNN) have been widely applied in many computer vision problems. These tasks are often conducted on input images with high quality without consideration of storage and transmission costs, making it necessary to ...

Learned image compression methods have represented the potential to outperform the traditional image compression methods in recent times. However, current learned image compression methods utilize the same spatial resolution for latent variables, ...