2 citations · 2 across the 1 of their papers we have counts for
4 papers
Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces
Hilmi E. Egilmez, Ankitesh K. Singh, Muhammed Coban +5
Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format. However, in order to achieve a superio…
Progressive Neural Image Compression with Nested Quantization and Latent Ordering
Yadong Lu, Yinhao Zhu, Yang Yang +2
We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstrea…
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning
Dana Kianfar, Auke Wiggers, Amir Said +2
Rate-Distortion Optimized Quantization (RDOQ) has played an important role in the coding performance of recent video compression standards such as H.264/AVC, H.265/HEVC, VP9 and AV…
Parametric Graph-based Separable Transforms for Video Coding
Hilmi E. Egilmez, Oguzhan Teke, Amir Said +2
In many video coding systems, separable transforms (such as two-dimensional DCT-2) have been used to code block residual signals obtained after prediction. This paper proposes a pa…