activity
20162026
most citedLearning Graphs with Monotone Topology Properties and Multiple Connected Components

55 citations · 125 across the 37 of their papers we have counts for

collaborators

45 papers

eess.IV2026

Rate-distortion optimization for full-reference image quality metrics via stochastic Hessian estimates

Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega

Block-based video codecs select coding parameters based on the input by optimizing a rate-distortion trade-off. The conventional distortion choice, the sum of squared errors (SSE),…

cs.LG2026

Spend Bits Where Queries Look: KV Cache Vector Quantization with Attention-Preserving Transforms

Samuel Fernández-Menduiña, Amir Ziashahabi, Eduardo Pavez +2

Long-context LLM decoding reads the key-value (KV) cache at every step. Loading it takes longer than computing attention over it, so throughput is bandwidth-bound. Hence, reducing…

eess.IV2026

Reduced-complexity Adaptive Loop Filtering via Input-dependent Graph Filters

Wen-Yang Lu, Eduardo Pavez, Antonio Ortega +2

Adaptive Loop Filtering is an important tool for suppressing compression artifacts in modern video codecs. In the enhanced compression model (ECM), a software test model used for e…

eess.IV2026

Motion Estimation Techniques for Volumetric Video Attribute Compression

Haoran Hong, Eduardo Pavez, Antonio Ortega +2

Point cloud compression relies on techniques to compress both geometry and attributes. Motion-based approaches for dynamic solid point cloud geometry compression within the geometr…

cs.MM2026

GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds

Rajrup Ghosh, Haodong Wang, Haoran Hong +6

Dynamic 3D Gaussian Splatting (3DGS) holds great promise as a 3D video streaming technology since it can represent complex 3D scenes with high fidelity. In this approach, every fra…

eess.IV2026

FaSST: Fast Sparsifying Secondary Transform

Darukeesan Pakiyarajah, Samuel Fernández-Menduiña, Eduardo Pavez +2

Data-dependent secondary transforms, which aim to decorrelate coefficients of a separable primary transform, can improve residual coding efficiency; however, their deployment is of…

eess.IV2026

Rate-Distortion Optimization for Ensembles of Non-Reference Metrics

Xin Xiong, Samuel Fernández-Menduiña, Eduardo Pavez +3

Non-reference metrics (NRMs) can assess the visual quality of images and videos without a reference, making them well-suited for the evaluation of user-generated content. Nonethele…

eess.IV2026

Wrapper-Aware Rate-Distortion Optimization in Feature Coding for Machines

Samuel Fernández-Menduiña, Hyomin Choi, Fabien Racapé +2

Feature coding for machines (FCM) is a lossy compression paradigm for split-inference. The transmitter encodes the outputs of the first part of a neural network before sending them…

eess.IV2025

INT-DTT+: Low-Complexity Data-Dependent Transforms for Video Coding

Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega +4

Discrete trigonometric transforms (DTTs), such as the DCT-2 and the DST-7, are widely used in video codecs for their balance between coding performance and computational efficiency…

eess.IV2025

Avoiding Quality Saturation in UGC Compression Using Denoised References

Xin Xiong, Samuel Fernández-Menduiña, Eduardo Pavez +3

Video-sharing platforms must re-encode large volumes of noisy user-generated content (UGC) to meet streaming demands. However, conventional codecs, which aim to minimize the mean s…

eess.IV2025

Region-Adaptive Learned Hierarchical Encoding for 3D Gaussian Splatting Data

Shashank N. Sridhara, Birendra Kathariya, Fangjun Pu +3

We introduce Region-Adaptive Learned Hierarchical Encoding (RALHE) for 3D Gaussian Splatting (3DGS) data. While 3DGS has recently become popular for novel view synthesis, the size…

eess.IV2025

Adaptive Voxelization for Transform coding of 3D Gaussian splatting data

Chenjunjie Wang, Shashank N. Sridhara, Eduardo Pavez +2

We present a novel compression framework for 3D Gaussian splatting (3DGS) data that leverages transform coding tools originally developed for point clouds. Contrary to existing 3DG…

eess.IV2025

Joint Optimization of Primary and Secondary Transforms Using Rate-Distortion Optimized Transform Design

Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega +4

Data-dependent transforms are increasingly being incorporated into next-generation video coding systems such as AVM, a codec under development by the Alliance for Open Media (AOM),…

eess.IV2025

Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression

Samuel Fernández-Menduiña, Xin Xiong, Eduardo Pavez +3

Service providers must encode a large volume of noisy videos to meet the demand for user-generated content (UGC) in online video-sharing platforms. However, low-quality UGC challen…

eess.IV2025★ 1 cited

Image Coding for Machines via Feature-Preserving Rate-Distortion Optimization

Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega

Many images and videos are primarily processed by computer vision algorithms, involving only occasional human inspection. When this content requires compression before processing,…

cs.LG2024

Towards joint graph learning and sampling set selection from data

Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega

We explore the problem of sampling graph signals in scenarios where the graph structure is not predefined and must be inferred from data. In this scenario, existing approaches rely…

eess.IV2024

Color-Guided Flying Pixel Correction in Depth Images

Ekamresh Vasudevan, Shashank N. Sridhara, Eduardo Pavez +3

We present a novel method to correct flying pixels within data captured by Time-of-flight (ToF) sensors. Flying pixel (FP) artifacts occur when signals from foreground and backgrou…

eess.IV2024

Graph-based Scalable Sampling of 3D Point Cloud Attributes

Shashank N. Sridhara, Eduardo Pavez, Ajinkya Jayawant +3

3D Point clouds (PCs) are commonly used to represent 3D scenes. They can have millions of points, making subsequent downstream tasks such as compression and streaming computational…

eess.SP2024

Graph-Based Signal Sampling with Adaptive Subspace Reconstruction for Spatially-Irregular Sensor Data

Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega

Choosing an appropriate frequency definition and norm is critical in graph signal sampling and reconstruction. Most previous works define frequencies based on the spectral properti…

eess.SP2024

Fast DCT+: A Family of Fast Transforms Based on Rank-One Updates of the Path Graph

Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega

This paper develops fast graph Fourier transform (GFT) algorithms with O(n log n) runtime complexity for rank-one updates of the path graph. We first show that several commonly-use…

eess.IV2024

Feature-Preserving Rate-Distortion Optimization in Image Coding for Machines

Samuel Fernández Menduiña, Eduardo Pavez, Antonio Ortega

With the increasing number of images and videos consumed by computer vision algorithms, compression methods are evolving to consider both perceptual quality and performance in down…

cs.CV2024

Full reference point cloud quality assessment using support vector regression

Ryosuke Watanabe, Shashank N. Sridhara, Haoran Hong +4

Point clouds are a general format for representing realistic 3D objects in diverse 3D applications. Since point clouds have large data sizes, developing efficient point cloud compr…

cs.CV2024

Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets

Ryosuke Watanabe, Keisuke Nonaka, Eduardo Pavez +2

Point clouds in 3D applications frequently experience quality degradation during processing, e.g., scanning and compression. Reliable point cloud quality assessment (PCQA) is impor…

eess.IV2024

Adaptive Online Learning of Separable Path Graph Transforms for Intra-prediction

Wen-Yang Lu, Eduardo Pavez, Antonio Ortega +2

Current video coding standards, including H.264/AVC, HEVC, and VVC, employ discrete cosine transform (DCT), discrete sine transform (DST), and secondary to Karhunen-Loeve transform…