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Found 7 papers, 0 papers with code
Adaptation and Attention for Neural Video Coding
In this work, we propose an end-to-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention.
Learning to Learn to Compress
In a second phase, the Model-Agnostic Meta-learning approach is adapted to the specific case of image compression, where the inner-loop performs latent tensor overfitting, and the outer loop updates both encoder and decoder neural networks based on the overfitting performance.
Efficient Adaptation of Neural Network Filter for Video Compression
We present an efficient finetuning methodology for neural-network filters which are applied as a postprocessing artifact-removal step in video coding pipelines.
End-to-End Learning for Video Frame Compression with Self-Attention
One of the core components of conventional (i. e., non-learned) video codecs consists of predicting a frame from a previously-decoded frame, by leveraging temporal correlations.
A Compression Objective and a Cycle Loss for Neural Image Compression
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss.
Compressing Weight-updates for Image Artifacts Removal Neural Networks
In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible.
Block-optimized Variable Bit Rate Neural Image Compression
In this work, we propose an end-to-end block-based auto-encoder system for image compression.
