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.
In this paper, we present an optimal reverse frame selection (RFS) algorithm based on dynamic programming
for delivering stored video under both bandwidth and buffer size constraints. Our objective is to find a feasible
set of frames that can maximize the video's accumulated motion metrics without violating any constraint. We
further extend RFS to solve the problem of video delivery over VBR channels where the channel bandwidth
is both limited and time-varying. In particular, we first run RFS offline for several bandwidth samples, and
the computation complexity is modest and scalable with the aids of frame size stuffing and non-optimal state
elimination. During online streaming, we only need to retrieve the optimal frame selection path from the pre-generated
offline results, and it can be applied to any VBR channels that can be modelled as piecewise CBR
channels. Experimental results show the good performance of our proposed algorithm.
Providing a certain quality of service (QoS) for multimedia
transmissions over a noisy wireless channel has always been a
challenge. The IEEE 802.11 standardization dedicates a working
group, group e, to investigate and propose a solution for enabling
IEEE 802.11 networks to provide multimedia transmissions with
certain QoS supports. As drafted in the latest draft release, the
IEEE 802.11e working group proposes the use of contention based
mechanism to achieve the transmissions of prioritized traffic, which
in turn provides a framework to support multimedia transmissions
over IEEE 802.11 networks. However, such a contention based priority
scheme does not deliver a strong QoS capability.
In this paper, we first study the characteristics of the IEEE
802.11e network. For all the four defined priorities of IEEE
802.11e, we first investigate their capacity characteristics. We
then design a resource allocation technique to better utilize the
bandwidth and improve the performance of video transmissions. Our
design uses a QoS mapping scheme according to the IEEE 802.11e
protocol characteristics to deliver scalable video. In addition, we
design an appropriate cross-layer video adaptation mechanism for the
scalable video that further improves the video quality combining
with our proposed resource allocation technique. We have evaluated
our proposed technique via simulations (NS2). We use PSNR as our
video quality measures. Our results show improvement in video
quality and resource usage when our proposed technique is
implemented.
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.
KEYWORDS: Image compression, Databases, Personal digital assistants, Mobile devices, Multimedia, General packet radio service, Telecommunications, Wireless communications, Mobile communications, Internet
With the availability of various wireless link-layer technologies,
such as Bluetooth, WLAN and GPRS, in one wireless device,
ubiquitous communications can be realized through managing
vertical handoff in the environment of wireless overlay networks.
In this paper, we propose a vertical handoff management system
based on mobile IPv6, which can automatically manage the multiple
network interfaces on the mobile device, and make decisions on
network interface selection according to the current situation.
Moreover, we apply our proposed vertical handoff management with
JPEG-2000 codec to the wireless application of map image access.
The developed system is able to provide seamless communications,
as well as fast retrieve any interested map region with any block
size, in different resolutions and different color representations
directly from the compressed bitstream.
In the case of high bit rate image transmission or having lots of
packets, the FEC (forward error correction) encoding and decoding
processes in the ULP (unequal loss protection) based schemes
should be applied to individual packet groups instead of all the
packets in order to avoid long processing delay. In this paper, we
propose a layered ULP (L-ULP) scheme for fast and efficient FEC
allocations among different packet groups and also within each
packet group. The numerical results show that the proposed L-ULP
scheme is quite promising for fast image transmission over packet
loss networks.
In the past, many schemes have been proposed for progressive image
transmission using unequal error protection (UEP) or unequal loss
protection (ULP). However, most existing UEP/ULP schemes do not
consider the minimum image quality requirement and usually have
high computation complexity. In this paper, we propose a layered
ULP (L-ULP) scheme for progressive image transmission over packet
loss channels, which is able to solve the mentioned problems of
existing ULP schemes by smartly choosing the layers. The numerical
results show that the proposed L-ULP scheme is quite promising for
fast image transmission over packet loss networks.
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.
We present a new proxy-based system to allow public use handheld devices to access instantly to the Low Resolution Picture Taking (LRPT) satellite weather image data and display regional weather image. First the location-based transcoding is done from non frame based satellite image to frame based CIF/QCIF image for handheld device. GPS information from expansion module is used in the location of the region of interest. A robust fixed-length joint source and channel coding scheme is then implemented to achieve robust wireless transmission. Experimental results show that the proposed system is suitable to the time varying, low bandwidth wireless channel and power constraint on wireless handheld devices.
KEYWORDS: Video, Video coding, Quantization, Computer programming, Distortion, Visualization, Low bit rate video, Video compression, Video processing, Data storage
Current rate control schemes in video coding standards do not have efficient frame-level bit allocation due to the limitation of real-time encoding. In this paper, by taking advantage of offline video encoding, we proposed a two-pass video encoding scheme for low bit rate streaming applications. Specifically, in the first pass, we generate the feature information of video sequences, including rate- distortion (R-D) functions and scene change boundaries. Then, in the second pass, according to the available channel bandwidth, by exploiting the feature information, we are able to implement frame-level bit allocation in an optimal way so that video sequences can be coded at low bit rate with an improved quality. Experimental results show the proposed scheme is able to achieve not only the improved PSNR results but also much smoother visual quality.
In this paper, we proposed a fixed-length robust joint source- channel coding (JSCC) scheme for image transmission over noisy channels. Three channel models are studied: binary symmetric channels (BSC) and additive white Gaussian noise (AWGN) channels for memoryless channels, and Gilbert-Elliott channels (GEC) for bursty channels. We derive, in this research, an explicit operational rate-distortion (R-D) function, which represents an end-to-end error measurement that includes errors due to both quantization and channel noise. In particular, we are able to incorporate the channel transition probability and channel bit error rate into the R-D function in the case of bursty channels. With the operational R-D function, bits are allocated not only among different subsources, but also between source coding and channel coding so that, under a fixed transmission rate, an optimum tradeoff between source coding accuracy and channel error protection can be achieved. This JSCC scheme is also integrated with allpass filtering source shaping to further improve the robustness against channel errors. Experimental results show that the proposed scheme can achieve not only high PSNR performance, but also excellent perceptual quality. Compared with the state-of-the-art JSCC schemes, this proposed scheme outperforms most of them especially when the channel mismatch occurs.
A combined source-channel coding scheme without explicit error protection is proposed to transmit images over noisy channels. Major components of the proposed coding scheme include 2-D DCT with block classification, fixed-length uniform threshold trellis coded quantization (UTTCQ), optimal bit allocation algorithm and noise reduction (NR) filters. The integration of these components allows us to organize the compressed bitstream in such a way that it is less sensitive to channel noise, and hence achieves data compression and error resilience at the same time. This paper reports our recent study by incorporating the block classification into the integrated scheme. Experimental results show that, in the case of noise-free channels and at the bit rate of 0.5 bpp, an improvement of 2.33 dB can be achieved with the classification. In the case of noisy channels, the gain is decreasing with the increasing of bit error rate to an average improvement of 0.46 dB with BER equals 0.1. Our proposed system uses no error protection, no synchronization codewords and no entropy coding. However, it shows decent compression ratio and gracious degradation with respect to increasing channel errors.
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