2 citations · 3 across the 2 of their papers we have counts for
5 papers
Boosting neural video codecs by exploiting hierarchical redundancy
Reza Pourreza, Hoang Le, Amir Said +2
In video compression, coding efficiency is improved by reusing pixels from previously decoded frames via motion and residual compensation. We define two levels of hierarchical redu…
A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space
Ankitesh K. Singh, Hilmi E. Egilmez, Reza Pourreza +3
Most of the existing deep learning based end-to-end video coding (DLEC) architectures are designed specifically for RGB color format, yet the video coding standards, including H.26…
Extending Neural P-frame Codecs for B-frame Coding
Reza Pourreza, Taco S Cohen
While most neural video codecs address P-frame coding (predicting each frame from past ones), in this paper we address B-frame compression (predicting frames using both past and fu…
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…
Feedback Recurrent Autoencoder for Video Compression
Adam Golinski, Reza Pourreza, Yang Yang +2
Recent advances in deep generative modeling have enabled efficient modeling of high dimensional data distributions and opened up a new horizon for solving data compression problems…