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Found 6 papers, 2 papers with code
Light-weight CNN-based VVC Inter Partitioning Acceleration
Compared to the High Efficiency Video Coding (HEVC) standard, VVC offers about 50% compression efficiency gain, in terms of Bjontegaard Delta-Rate (BD-rate), at the cost of about 10x more encoder complexity.
CNN-based Prediction of Partition Path for VVC Fast Inter Partitioning Using Motion Fields
Experiments show that the proposed method can achieve acceleration ranging from 16. 5% to 60. 2% under the RandomAccess Group Of Picture 32 (RAGOP32) configuration with a reasonable efficiency drop ranging from 0. 44% to 4. 59% in terms of BD-rate, which surpasses other state-of-the-art solutions.
OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression
State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs).
Fine granularity access in interactive compression of 360-degree images based on rate-adaptive channel codes
This requires two characteristics: efficient compression of data, to lower the storage cost, and random access ability to extract part of the compressed stream requested by the user (for reducing the transmission rate).
Context-adaptive neural network based prediction for image compression
Unlike the H. 265 intra prediction modes, which are each specialized in predicting a specific texture, the proposed PNNS can model a large set of complex textures.
Autoencoder based image compression: can the learning be quantization independent?
Usually, the rate-distortion performances of image compression are tuned by varying the quantization step size.
