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Found 6 papers, 2 papers with code
Estimating the Resize Parameter in End-to-end Learned Image Compression
By conducting extensive experimental tests on existing deep image compression models, we show results that our new resizing parameter estimation framework can provide Bj{\o}ntegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
Convolutional Block Design for Learned Fractional Downsampling
The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values.
Perceptually Optimizing Deep Image Compression
Mean squared error (MSE) and $\ell_p$ norms have largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties.
ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression
By building on top of an existing deep image compression model, we are able to demonstrate a bitrate reduction of as much as $31\%$ over MSE optimization, given a specified perceptual quality (VMAF) level.
Adversarial Video Compression Guided by Soft Edge Detection
We propose a video compression framework using conditional Generative Adversarial Networks (GANs).
Learning to Predict Streaming Video QoE: Distortions, Rebuffering and Memory
Mobile streaming video data accounts for a large and increasing percentage of wireless network traffic.
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