no code implementations • 26 Mar 2024 • Qiqi Hou, Farzad Farhadzadeh, Amir Said, Guillaume Sautiere, Hoang Le
The rise of new video modalities like virtual reality or autonomous driving has increased the demand for efficient multi-view video compression methods, both in terms of rate-distortion (R-D) performance and in terms of delay and runtime.
no code implementations • 1 Dec 2023 • Amir Said, Hoang Le, Farzad Farhadzadeh
Video compression systems must support increasing bandwidth and data throughput at low cost and power, and can be limited by entropy coding bottlenecks.
no code implementations • 1 Dec 2023 • Amir Said
We propose a new class of kernels to simplify the design of filters for image interpolation and resizing.
no code implementations • 2 Oct 2023 • Ties van Rozendaal, Tushar Singhal, Hoang Le, Guillaume Sautiere, Amir Said, Krishna Buska, Anjuman Raha, Dimitris Kalatzis, Hitarth Mehta, Frank Mayer, Liang Zhang, Markus Nagel, Auke Wiggers
This work presents the first neural video codec that decodes 1080p YUV420 video in real time on a mobile device.
no code implementations • 24 Jan 2023 • Amir Said, Manish Kumar Singh, Reza Pourreza
Neural networks (NN) can improve standard video compression by pre- and post-processing the encoded video.
no code implementations • 20 Jan 2023 • Amir Said, Reza Pourreza, Hoang Le
Neural-based image and video codecs are significantly more power-efficient when weights and activations are quantized to low-precision integers.
no code implementations • 8 Aug 2022 • Reza Pourreza, Hoang Le, Amir Said, Guillaume Sautiere, Auke Wiggers
In video compression, coding efficiency is improved by reusing pixels from previously decoded frames via motion and residual compensation.
no code implementations • 18 Jul 2022 • Hoang Le, Liang Zhang, Amir Said, Guillaume Sautiere, Yang Yang, Pranav Shrestha, Fei Yin, Reza Pourreza, Auke Wiggers
Realizing the potential of neural video codecs on mobile devices is a big technological challenge due to the computational complexity of deep networks and the power-constrained mobile hardware.
no code implementations • 27 Feb 2021 • Hilmi E. Egilmez, Ankitesh K. Singh, Muhammed Coban, Marta Karczewicz, Yinhao Zhu, Yang Yang, Amir Said, Taco S. Cohen
Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format.
no code implementations • 4 Feb 2021 • Yadong Lu, Yinhao Zhu, Yang Yang, Amir Said, Taco S Cohen
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 bitstream.
no code implementations • 11 Dec 2020 • Dana Kianfar, Auke Wiggers, Amir Said, Reza Pourreza, Taco Cohen
We train two classes of neural networks, a fully-convolutional network and an auto-regressive network, and evaluate each as a post-quantization step designed to refine cheap quantization schemes such as scalar quantization (SQ).
no code implementations • 16 Nov 2019 • Hilmi E. Egilmez, Oguzhan Teke, Amir Said, Vadim Seregin, Marta Karczewicz
In many video coding systems, separable transforms (such as two-dimensional DCT-2) have been used to code block residual signals obtained after prediction.