Search Results for author:
Found 22 papers, 2 papers with code
Headset: Human emotion awareness under partial occlusions multimodal dataset
The dataset can be helpful in the evaluation and performance testing of various XR algorithms, including but not limited to facial expression recognition and reconstruction, facial reenactment, and volumetric video.
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.
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression
Light field is a type of image data that captures the 3D scene information by recording light rays emitted from a scene at various orientations.
Learning-based Spatial and Angular Information Separation for Light Field Compression
Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations.
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression
While existing compression methods encode the set of light field sub-aperture images, our proposed method learns an implicit scene representation in the form of a Neural Radiance Field (NeRF), which also enables view synthesis.
PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent
We show that it is possible to train a network directly modeling the gradient of a MAP regularizer while jointly training the corresponding MAP denoiser.
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration
Plug-and-Play optimization recently emerged as a powerful technique for solving inverse problems by plugging a denoiser into a classical optimization algorithm.
A learning-based view extrapolation method for axial super-resolution
Axial light field resolution refers to the ability to distinguish features at different depths by refocusing.
A Lightweight Neural Network for Monocular View Generation with Occlusion Handling
The network is also able to identify these occluded areas at training and at test time by checking the pixelwise left-right consistency of the produced disparity maps.
Geometry-Aware Graph Transforms for Light Field Compact Representation
Both non separable and separable transforms are considered.
Prediction and Sampling with Local Graph Transforms for Quasi-Lossless Light Field Compression
The proposed approach is investigated and is shown to be very efficient in the context of spatio-angular transforms for quasi-lossless compression of light fields.
A Fourier Disparity Layer representation for Light Fields
In this paper, we present a new Light Field representation for efficient Light Field processing and rendering called Fourier Disparity Layers (FDL).
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.
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification
A multilevel tree-structured discriminative dictionary is learnt for each class, with a learning objective concerning the reconstruction errors of the image patches around the pixels over each class-representative dictionary.
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.
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks
Light field imaging has recently known a regain of interest due to the availability of practical light field capturing systems that offer a wide range of applications in the field of computer vision.
Scalable image coding based on epitomes
In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome.
Face Hallucination using Linear Models of Coupled Sparse Support
Most face super-resolution methods assume that low-resolution and high-resolution manifolds have similar local geometrical structure, hence learn local models on the lowresolution manifolds (e. g. sparse or locally linear embedding models), which are then applied on the high-resolution manifold.
A study of the classification of low-dimensional data with supervised manifold learning
Supervised manifold learning methods learn data representations by preserving the geometric structure of data while enhancing the separation between data samples from different classes.
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Local learning of sparse image models has proven to be very effective to solve inverse problems in many computer vision applications.
Out-of-sample generalizations for supervised manifold learning for classification
Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the learned embedding.
