107 citations · 189 across the 11 of their papers we have counts for
13 papers
Position Dependent Prediction Combination For Intra-Frame Video Coding
Amir Said, Xin Zhao, Marta Karczewicz +2
Intra-frame prediction in the High Efficiency Video Coding (HEVC) standard can be empirically improved by applying sets of recursive two-dimensional filters to the predicted values…
Low-Complexity Transform Adjustments For Video Coding
Amir Said, Hilmi E. Egilmez, Yung-Hsuan Chao
Recent video codecs with multiple separable transforms can achieve significant coding gains using asymmetric trigonometric transforms (DCTs and DSTs), because they can exploit dive…
Highly Efficient Non-Separable Transforms for Next Generation Video Coding
Amir Said, Xin Zhao, Marta Karczewicz +3
For the last few decades, the application of signal-adaptive transform coding to video compression has been stymied by the large computational complexity of matrix-based solutions.…
New Filters for Image Interpolation and Resizing
Amir Said
We propose a new class of kernels to simplify the design of filters for image interpolation and resizing. Their properties are defined according to two parameters, specifying the w…
Introduction to Arithmetic Coding -- Theory and Practice
Amir Said
This introduction to arithmetic coding is divided in two parts. The first explains how and why arithmetic coding works. We start presenting it in very general terms, so that its si…
Differentiable bit-rate estimation for neural-based video codec enhancement
Amir Said, Manish Kumar Singh, Reza Pourreza
Neural networks (NN) can improve standard video compression by pre- and post-processing the encoded video. For optimal NN training, the standard codec needs to be replaced with a c…
Optimized learned entropy coding parameters for practical neural-based image and video compression
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. While there are general-purpose tec…
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…
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices
Hoang Le, Liang Zhang, Amir Said +6
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 mob…
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…