KEYWORDS: Motion estimation, Computer programming, Video coding, Video, Computing systems, Video surveillance, Quantization, Video compression, Switches, Linear filtering
Distributed video coding is a new video coding paradigm that shifts the computational intensive motion estimation
from encoder to decoder. This results in a lightweight encoder and a complex decoder, as opposed
to the predictive video coding scheme (e.g., MPEG-X and H.26X) with a complex encoder and a lightweight
decoder. Both schemas, however, do not have the ability to adapt to varying complexity constraints imposed by
encoder and decoder, which is an essential ability for applications targeting a wide range of devices with different
complexity constraints or applications with temporary variable complexity constraints. Moreover, the effect of
complexity adaptation on the overall compression performance is of great importance and has not yet been investigated.
To address this need, we have developed a video coding system with the possibility to adapt itself to
complexity constraints by dynamically sharing the motion estimation computations between both components.
On this system we have studied the effect of the complexity distribution on the compression performance.
This paper describes how motion estimation can be shared using heuristic dynamic complexity and how
distribution of complexity affects the overall compression performance of the system. The results show that the
complexity can indeed be shared between encoder and decoder in an efficient way at acceptable rate-distortion performance.
In order to be able to better cope with packet loss, H.264/AVC, besides offering superior coding efficiency, also comes with a number of error resilience tools. The goal of these tools is to enable the decoding of a bitstream containing encoded video, even when parts of it are missing. On top of that, the visual quality of the decoded video should remain as high as possible. In this paper, we will discuss and evaluate one of these tools, in particular the data partitioning tool. Experimental results will show that using data partitioning can significantly improve the quality of a video sequence when packet loss occurs. However, this is only possible if the channel used for transmitting the video allows selective protection of the different data partitions. In the most extreme case, an increase in PSNR of up to 9.77 dB can be achieved. This paper will also show that the overhead caused by using data partitioning is acceptable. In terms of bit rate, the overhead amounts to approximately 13 bytes per slice. In general, this is less than 1% of the total bit rate. On top of that, using constrained intra prediction, which is required to fully exploit data partitioning, causes a decrease in quality of about 0.5 dB for high quality video and between 1 and 2 dB for low quality video.
With all the hype created around multimedia in the last few years, consumers expect to be able to access multimedia content in a real-time manner, anywhere and anytime. One of the problems with the real-time requirement is that transportation networks, such as the Internet, are still prone to errors. Due to real-time constraints, retransmission of lost data is, more often than not, not an option. Therefore, the study of error resilience and error concealment techniques is of the utmost importance since it can seriously limit the impact of a transmission error. In this paper an evaluation of a part of flexible macroblock ordering, one of the new error resilience techniques in H.264/AVC, is made by analyzing its costs and gains in an error-prone environment. This paper concentrates on the study of flexible macroblock ordering (FMO). More specifically a study of scattered slices, FMO type 1, is made. Our analysis shows that FMO type 1 is a good tool to introduce error robustness into an H.264/AVC bitstream as long as the QP is higher than 30. When the QP of the bitstream is below 30, the cost of FMO type 1 becomes a serious burden.
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