Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
Delta-ICM: Entropy Modeling with Delta Function for Learned Image Compression
Image Coding for Machines (ICM) is becoming more important as research in computer vision progresses. ICM is a vital research field that pursues the use of images for image recognition models, facilitating efficient imag…
Image CompressionCompressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
The rapid advancement of artificial intelligence (AI) in weather research has been driven by the ability to learn from large, high-dimensional datasets. However, this progress also poses significant challenges, particula…
Image CompressionJPEG Inspired Deep Learning
Although it is traditionally believed that lossy image compression, such as JPEG compression, has a negative impact on the performance of deep neural networks (DNNs), it is shown by recent works that well-crafted JPEG co…
Deep LearningFine-Grained Image Classificationimage-classificationImage Classification+2Toward Scalable Image Feature Compression: A Content-Adaptive and Diffusion-Based Approach
Traditional image codecs emphasize signal fidelity and human perception, often at the expense of machine vision tasks. Deep learning methods have demonstrated promising coding performance by utilizing rich semantic embed…
Data CompressionFacial Landmark DetectionFeature CompressionImage Compression+4Causal Context Adjustment Loss for Learned Image Compression
In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniques are VAE-based with an autoregressive…
Image CompressionRDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion
Diffusion-based extreme image compression methods have achieved impressive performance at extremely low bitrates. However, constrained by the iterative denoising process that starts from pure noise, these methods are lim…
DenoisingImage CompressionGABIC: Graph-based Attention Block for Image Compression
While standardized codecs like JPEG and HEVC-intra represent the industry standard in image compression, neural Learned Image Compression (LIC) codecs represent a promising alternative. In detail, integrating attention m…
Image CompressionCOSMIC: Compress Satellite Images Efficiently via Diffusion Compensation
With the rapidly increasing number of satellites in space and their enhanced capabilities, the amount of earth observation images collected by satellites is exceeding the transmission limits of satellite-to-ground links.…
Earth ObservationImage CompressionSTanH : Parametric Quantization for Variable Rate Learned Image Compression
In end-to-end learned image compression, encoder and decoder are jointly trained to minimize a $R + {\lambda}D$ cost function, where ${\lambda}$ controls the trade-off between rate of the quantized latent representation …
DecoderImage CompressionQuantizationPerCo (SD): Open Perceptual Compression
We introduce PerCo (SD), a perceptual image compression method based on Stable Diffusion v2.1, targeting the ultra-low bit range. PerCo (SD) serves as an open and competitive alternative to the state-of-the-art method Pe…
AttributeImage CompressionAll-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation
Image coding for multi-task applications, catering to both human perception and machine vision, has been extensively investigated. Existing methods often rely on multiple task-specific encoder-decoder pairs, leading to h…
AllData CompressionFeature ImportanceImage Classification+2Learning-Based Image Compression for Machines
While learning based compression techniques for images have outperformed traditional methods, they have not been widely adopted in machine learning pipelines. This is largely due to lack of standardization and lack of re…
Image CompressionStreaming Neural Images
Implicit Neural Representations (INRs) are a novel paradigm for signal representation that have attracted considerable interest for image compression. INRs offer unprecedented advantages in signal resolution and memory e…
Image CompressionThe Effect of Lossy Compression on 3D Medical Images Segmentation with Deep Learning
Image compression is a critical tool in decreasing the cost of storage and improving the speed of transmission over the internet. While deep learning applications for natural images widely adopts the usage of lossy compr…
Image CompressionAIM 2024 Challenge on UHD Blind Photo Quality Assessment
We introduce the AIM 2024 UHD-IQA Challenge, a competition to advance the No-Reference Image Quality Assessment (NR-IQA) task for modern, high-resolution photos. The challenge is based on the recently released UHD-IQA Be…
4kComputational EfficiencyImage CompressionImage Quality Assessment+3Window-based Channel Attention for Wavelet-enhanced Learned Image Compression
Learned Image Compression (LIC) models have achieved superior rate-distortion performance than traditional codecs. Existing LIC models use CNN, Transformer, or Mixed CNN-Transformer as basic blocks. However, limited by t…
Image CompressionReduced bit median quantization: A middle process for Efficient Image Compression
Image compression techniques have made remarkable progress when it comes to file size reduction with a tolerable quality reduction; nonetheless, they are facing some challenges when it comes to applying more compression …
Image CompressionQuantizationMulti-Scale Feature Prediction with Auxiliary-Info for Neural Image Compression
Recently, significant improvements in rate-distortion performance of image compression have been achieved with deep-learning techniques. A key factor in this success is the use of additional bits to predict an approximat…
Image Compressionparameter estimationVideo CompressionBreaking the Barriers of One-to-One Usage of Implicit Neural Representation in Image Compression: A Linear Combination Approach with Performance Guarantees
In an era where the exponential growth of image data driven by the Internet of Things (IoT) is outpacing traditional storage solutions, this work explores and advances the potential of Implicit Neural Representation (INR…
Image CompressionLFIC-DRASC: Deep Light Field Image Compression Using Disentangled Representation and Asymmetrical Strip Convolution
Light-Field (LF) image is emerging 4D data of light rays that is capable of realistically presenting spatial and angular information of 3D scene. However, the large data volume of LF images becomes the most challenging i…
Image Compression