C. Andrew Segall
Northwestern University
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Featured researches published by C. Andrew Segall.
IEEE Journal of Selected Topics in Signal Processing | 2013
Gary J. Sullivan; Jill M. Boyce; Ying Chen; Jens-Rainer Ohm; C. Andrew Segall; Anthony Vetro
This paper describes extensions to the High Efficiency Video Coding (HEVC) standard that are active areas of current development in the relevant international standardization committees. While the first version of HEVC is sufficient to cover a wide range of applications, needs for enhancing the standard in several ways have been identified, including work on range extensions for color format and bit depth enhancement, embedded-bitstream scalability, and 3D video. The standardization of extensions in each of these areas will be completed in 2014, and further work is also planned. The design for these extensions represents the latest state of the art for video coding and its applications.
IEEE Journal of Selected Topics in Signal Processing | 2013
Kiran Misra; C. Andrew Segall; Michael Horowitz; Shilin Xu; Arild Fuldseth; Minhua Zhou
Tiles is a new feature in the High Efficiency Video Coding (HEVC) standard that divides a picture into independent, rectangular regions. This division provides a number of advantages. Specifically, it increases the “parallel friendliness” of the new standard by enabling improved coding efficiency for parallel architectures, as compared to previous sliced based methods. Additionally, tiles facilitate improved maximum transmission unit (MTU) size matching, reduced line buffer memory, and additional region-of-interest functionality. In this paper, we introduce the tiles feature and survey the performance of the tool. Coding efficiency is reported for different parallelization factors and MTU size requirements. Additionally, a tile-based region of interest coding method is developed.
international conference on image processing | 2007
C. Andrew Segall
A method for coding high dynamic range video sequences is considered. The technique is scalable, in that it facilitates the simultaneous transmission of standard and high dynamic range versions of the sequence in a single bit-stream. Furthermore, the approach is backwards compatible with the existing, state-of-the-art, AVC|H.264 video coding standard. Emphasis is placed on improved coding efficiency as well as managed computational complexity. Results illustrate the efficacy of the approach.
international conference on image processing | 2007
Gary J. Sullivan; Haoping Yu; Shunichi Sekiguchi; Huifang Sun; Thomas Wedi; Steffen Wittmann; Yung-Lyul Lee; C. Andrew Segall; Teruhiko Suzuki
To support high quality video applications, the Joint Video Team (JVT) has recently added five new profiles, two new supplemental enhancement information (SEI) messages, and two new extended gamut color space indicators to the MPEG4-AVC/H.264 video coding standard. The new profiles include substantial feature enhancements for high-quality video applications, including improved-efficiency 4:4:4 video format coding, improved-efficiency lossless macroblock coding, coding 4:4:4 video pictures using three separately-coded color planes, and support of bit depths up to 14 bits per sample. The new features were developed to support a wide range of applications where high quality video compression is demanded, including professional and semi-professional scenarios in particular. They also anticipate the introduction of higher fidelity displays. In this paper, the new extensions are presented along with quantitaive estimates of the benefits of the new features and a discussion o the target aplication environments.
Pattern Recognition | 2000
Rafael Molina; Aggelos K. Katsaggelos; Javier Mateos; Aurora Hermoso; C. Andrew Segall
Abstract Over the last few years, a growing number of researchers from varied disciplines have been utilizing Markov random fields (MRF) models for developing optimal, robust algorithms for various problems, such as texture analysis, image synthesis, classification and segmentation, surface reconstruction, integration of several low level vision modules, sensor fusion and image restoration. However, no much work has been reported on the use of Simulated Annealing (SA) and Iterative Conditional Mode (ICM) algorithms for maximum a posteriori estimation in image restoration problems when severe blurring is present. In this paper we examine the use of compound Gauss–Markov random fields (CGMRF) to restore severely blurred high range images. For this deblurring problem, the convergence of the SA and ICM algorithms has not been established. We propose two new iterative restoration algorithms which can be considered as extensions of the classical SA and ICM approaches and whose convergence is established. Finally, they are tested on real and synthetic images and the results compared with the restorations obtained by other iterative schemes.
international conference on image processing | 2008
C. Andrew Segall; Jie Zhao
The scalable video coding (SVC) extension to the H.264/AVC video coding standard introduces multiple functionalities to the H.264/AVC decoding process. These functionalities include spatial, quality and temporal scalability. In this document, we introduce an additional capability that is supported by the SVC extentions. This feature is commonly referred to as bit-stream rewriting, and it allows a multiple layer, scalable bit-stream to be converted to a single layer, H.264/AVC compliant bit-steam without loss and without reconstruction of image intensity data.
international conference on image processing | 2006
C. Andrew Segall; Aggelos K. Katsaggelos
Resampling is a fundamental issue in the design of a spatially scalable video codec. The resampling procedure is responsible for down-sampling the high-resolution video sequence to generate lower resolution data, as well as upsampling the transmitted lower resolution data to predict the original high-resolution frames. In both cases, the resampling operation must make trade-offs between coding efficiency, image quality and computational complexity. In this paper, we consider the resampling design problem within an optimization framework.
international conference on image processing | 2011
Zhan Ma; C. Andrew Segall
In this paper, we propose a novel hybrid frame buffer compression algorithm to reduce the memory bandwidth for low-power video coding. In our work, we first decompose the full-resolution image into low resolution (LR) and high resolution (HR) components. We then calculate the HR residual by taking the difference between original HR pixel and an estimate derived from surrounding LR pixels. Finally, we use absolute moment block truncation coding to quantize and compress the LR pixel and HR residual data so as to reduce the memory bandwidth. We integrate our approach into the JCT-VC reference software for High Efficiency Video Coding (HEVC). Results show negligible impact on coding efficiency with significant memory bandwidth reduction. Specifically, we observe a bit rate increase of 0.38% and 1% with 21% and 31% memory bandwidth reduction, respectively.
Archive | 2002
C. Andrew Segall; Aggelos K. Katsaggelos; Rafael Molina; Javier Mateos
The problem of recovering a high-resolution frame from a sequence of low-resolution and compressed images is considered. The presence of the compression system complicates the recovery problem, as the operation reduces the amount of frequency aliasing in the low-resolution frames and introduces a non-linear noise process. Increasing the resolution of the decoded frames can still be addressed in a recovery framework though, but the method must also include knowledge of the underlying compression system. Furthermore, improving the spatial resolution of the decoded sequence is no longer the only goal of the recovery algorithm. Instead, the technique is also required to attenuate compression artifacts.
visual communications and image processing | 2000
C. Andrew Segall; Passant V. Karunaratne; Aggelos K. Katsaggelos
Pre-processing algorithms improve on the performance of a video compression system by removing spurious noise and insignificant features from the original images. This increases compression efficiency and attenuates coding artifacts. Unfortunately, determining the appropriate amount of pre-filtering is a difficult problem, as it depends on both the content of an image as well as the target bit-rate of compression algorithm. In this paper, we explore a pre- processing technique that is loosely coupled to the quantization decisions of a rate control mechanism. This technique results in a pre-processing system that operates directly on the Displaced Frame Difference (DFD) and is applicable to any standard-compatible compression system. Results explore the effect of several standard filters on the DFD. An adaptive technique is then considered.