443 research outputs found
Investigating low-bitrate, low-complexity H.264 region of interest techniques in error-prone environments
The H.264/AVC video coding standard leverages advanced compression methods to provide a significant increase in performance over previous CODECs in terms of picture quality, bitrate, and flexibility. The specification itself provides several profiles and levels that allow customization through the use of various advanced features. In addition to these features, several new video coding techniques have been developed since the standard\u27s inception. One such technique known as Region of Interest (RoI) coding has been in existence since before H.264\u27s formalization, and several means of implementing RoI coding in H.264 have been proposed. Region of Interest coding operates under the assumption that one or more regions of a sequence have higher priority than the rest of the video. One goal of RoI coding is to provide a decrease in bitrate without significant loss of perceptual quality, and this is particularly applicable to low complexity environments, if the proper implementation is used. Furthermore, RoI coding may allow for enhanced error resilience in the selected regions if desired, making RoI suitable for both low-bitrate and error-prone scenarios. The goal of this thesis project was to examine H.264 Region of Interest coding as it applies to such scenarios. A modified version of the H.264 JM Reference Software was created in which all non-Baseline profile features were removed. Six low-complexity RoI coding techniques, three targeting rate control and three targeting error resilience, were selected for implementation. Error and distortion modeling tools were created to enhance the quality of experimental data. Results were gathered by varying a range of coding parameters including frame size, target bitrate, and macroblock error rates. Methods were then examined based on their rate-distortion curves, ability to achieve target bitrates accurately, and per-region distortions where applicable
Deep Video Codec Control
Lossy video compression is commonly used when transmitting and storing video
data. Unified video codecs (e.g., H.264 or H.265) remain the de facto standard,
despite the availability of advanced (neural) compression approaches.
Transmitting videos in the face of dynamic network bandwidth conditions
requires video codecs to adapt to vastly different compression strengths. Rate
control modules augment the codec's compression such that bandwidth constraints
are satisfied and video distortion is minimized. While, both standard video
codes and their rate control modules are developed to minimize video distortion
w.r.t. human quality assessment, preserving the downstream performance of deep
vision models is not considered. In this paper, we present the first end-to-end
learnable deep video codec control considering both bandwidth constraints and
downstream vision performance, while not breaking existing standardization. We
demonstrate for two common vision tasks (semantic segmentation and optical flow
estimation) and on two different datasets that our deep codec control better
preserves downstream performance than using 2-pass average bit rate control
while meeting dynamic bandwidth constraints and adhering to standardizations.Comment: 22 pages, 26 figures, 6 table
Study and simulation of low rate video coding schemes
The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design
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