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Lossy Image Compression with Foundation Diffusion Models
Incorporating diffusion models in the image compression domain has the potential to produce realistic and detailed reconstructions, especially at extremely low bitrates.
Video Compression With Entropy-Constrained Neural Representations
This performance gap can be explained by the fact that current NVR methods: i) use architectures that do not efficiently obtain a compact representation of temporal and spatial information; and ii) minimize rate and distortion disjointly (first overfitting a network on a video and then using heuristic techniques such as post-training quantization or weight pruning to compress the model).
Microdosing: Knowledge Distillation for GAN based Compression
Recently, significant progress has been made in learned image and video compression.
Bridging the Gap between Semantics and Multimedia Processing
In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap.
