KEYWORDS: Video, Computer programming, Video coding, Distortion, Smoothing, Linear filtering, Video compression, Detection and tracking algorithms, Quantization, Visualization
For real-time video streaming applications over the constant bit rate channels, it is highly desired that video
signals can be encoded in not only good average quality but also smooth video quality. However, in the case
that the network resource is sufficiently large and the video quality has reached the target quality, the quality
smoothing is not necessary and the rate smoothing is desired to avoid overusing the unnecessary network resource
but also achieve a smoothed traffic rate. In this paper, we propose a novel real-time rate-smoothed encoding
scheme by applying the low pass filtering idea. Both theoretical analysis and experimental results show that
the proposed rate-smoothed encoding scheme can achieve a target average quality while significantly reducing
the peak rate and the rate variance. We have further proposed a joint quality and rate smoothed encoding
scheme, which can provide adaptive smoothing according to different situations. Experimental results show that
the proposed joint smoothing scheme can make an optimal balance between the quality fluctuation and the rate
fluctuation, and hence improve the overall system performance.
Leaky prediction based FGS (Fine Granularity Scalability) can achieve better coding efficiency than the baseline
FGS. However, for leaky prediction based FGS (L-FGS), constant quality constrained bit allocation, i.e., how to
optimally allocate bits given the current channel bandwidth, is still an open problem. In this paper, based on the
accurate R-D (Rate-Distortion) model developed in our previous work, we propose a constant quality constrained
bit allocation scheme for L-FGS. The proposed scheme is a combination of offline and online processes. During
the offline stage, we perform the L-FGS encoding and collect the necessary feature information. At the online
stage, given the transmission bandwidth at that time, we quickly estimate the R-D curves of a sequence of
consecutive video frames based on our previously developed R-D model and then perform the corresponding
bit allocation using a sliding window technique. Experimental results show that our proposed bit allocation
algorithm can achieve much more smooth video quality than the traditional uniform bit allocation under both
CBR (constant bit rate) and VBR (variable bit rate) channels.
FGS (Fine Granularity Scalability) is a scalable coding technique
which can provide flexibility and good performance for Internet
video streaming. However, FGS is not suitable for wireless video
streaming. This is mainly because the low coding efficiency of FGS
does not fit the limited bandwidth of wireless networks. In this
paper, we jointly consider mode selection and UEP (unequal error
protection) for FGS video transmission over wireless channels. In
particular, we provide two modes for encoding the FGS enhancement
layer of each video frame, i.e., with prediction or without
prediction. The mode selection depends on the capability of UEP
while the solution for UEP depends on the vulnerability of source
data. We construct an overall end-to-end rate-distortion (R-D)
function. Based on this end-to-end R-D function, we are able to
find the optimal solutions for both mode selection and UEP so that
an optimal tradeoff between efficiency and robustness can be
achieved. Experimental results demonstrate the proposed system is
able to significantly improve the end-to-end video quality for
wireless FGS video coding and transmission.
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