MS-SSIM
53 papers with code • 1 benchmarks • 0 datasets
A MS-SSIM score helps to analyze how much a De-warping module has been able to de-warp a document image from its initial distorted view.
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Use these libraries to find MS-SSIM models and implementationsLatest papers with no code
Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression
With the SDD-based motion model and long short-term temporal contexts fusion, our proposed learned video codec can obtain more accurate inter prediction.
Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression
Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements.
Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding
While convolution and self-attention are extensively used in learned image compression (LIC) for transform coding, this paper proposes an alternative called Contextual Clustering based LIC (CLIC) which primarily relies on clustering operations and local attention for correlation characterization and compact representation of an image.
End-to-End Optimized Image Compression with the Frequency-Oriented Transform
Image compression constitutes a significant challenge amidst the era of information explosion.
VALD-MD: Visual Attribution via Latent Diffusion for Medical Diagnostics
Visual attribution in medical imaging seeks to make evident the diagnostically-relevant components of a medical image, in contrast to the more common detection of diseased tissue deployed in standard machine vision pipelines (which are less straightforwardly interpretable/explainable to clinicians).
A Relay System for Semantic Image Transmission based on Shared Feature Extraction and Hyperprior Entropy Compression
Nowadays, the need for high-quality image reconstruction and restoration is more and more urgent.
Multiscale Motion-Aware and Spatial-Temporal-Channel Contextual Coding Network for Learned Video Compression
In addition, most approaches ignore the spatial and channel redundancy.
How to Evaluate Semantic Communications for Images with ViTScore Metric?
To evaluate the performance of ViTScore, we compare ViTScore with 3 typical metrics (PSNR, MS-SSIM, and LPIPS) through 4 classes of experiments: (i) correlation with BERTScore through evaluation of image caption downstream CV task, (ii) evaluation in classical image communications, (iii) evaluation in image semantic communication systems, and (iv) evaluation in image semantic communication systems with semantic attack.
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation
Then we only encode non-zero channels in the encoding and decoding process, which can greatly reduce the encoding and decoding time.
Fusion of Infrared and Visible Images based on Spatial-Channel Attentional Mechanism
In the study, we present AMFusionNet, an innovative approach to infrared and visible image fusion (IVIF), harnessing the power of multiple kernel sizes and attention mechanisms.
