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JOINT VIDEO/DEPTH/FEC RATE ALLOCATION WITH CONSIDERING 3D VISUALSALIENCY FOR SCALABLE 3D VIDEO STREAMING

Yanwei Liu1

, Jinxia Liu3

, Song Ci1,2

, Yun Ye2

1Institute of Acoustics, Chinese Academy of Sciences, Beijing, China2University of Nebraska-Lincoln, Omaha, NE 68182 USA

3Zhejiang Wanli University, Ningbo, [email protected]

ABSTRACT

For robust video plus depth based 3D video streaming,video, depth and packet-level forward error correction (FEC)can provide many rate combinations with various 3D vi-

sual qualities to adapt to the dynamic channel condition-s. Video/depth/FEC rate allocation under the channel band-width constraint is an important optimization problem forrobust 3D video streaming. This paper proposes a join-t video/depth/FEC rate allocation method by maximizing thereceivers 3D visual quality. Through predicting the percep-tual 3D visual qualities of the different video/depth/FEC ratecombinations, the optimal GOP-level video/depth/FEC ratecombination can be found. Further, the selected FEC ratesare unequally assigned to different levels of 3D saliency re-gions within each video/depth frame. The effectiveness of theproposed 3D saliency based joint video/depth/FEC rate allo-cation method for scalable 3D video streaming is validated byextensive experimental results.

Index Terms Video/depth rate allocation, FEC, 3Dsaliency, scalable 3D video streaming

1. INTRODUCTION

In recent years, 3D video is very popular due to its pleasing3D visual experience which 2D plane video can not provide.With the help of 3D glasses, 3D video has moved into homefrom the theater by enabling 3DTV. Currently, 3DTV can bedelivered through many ways, such as broadcasting, Blu-ray

disk, cable TV, Internet and so on [1]. Among them, 3D videodistribution over the broadband Internet is a promising way toprovide the VOD-like 3D services. With the rapid progressesin mobile Internet, most trafcs originally running over thewired networks are gradually extending into the mobile Inter-net due to its anytime and anywhere feature. Hence, as one

This work was supported in part by Important National Science andTechnology Specic Project under contracts 2012ZX03003006-004 and2012ZX03002008, NSFC under grant No. 61102077, Ningbo Natural Sci-ence Foundation under contract 2012A610044, Zhejiang Provincial NaturalScience Foundation of China under contract LY13F010012 and NSF undergrant Nos.1145596 and 0830493.

of the next generation multimedia form, a large part of 3Dvideo will be distributed over the mobile Internet by enablingthe mobile 3DTV in the future [2].

Based on different application considerations, there aremany data formats to represent 3D video [3]. Among theseformats, video plus depth is a simple format to represent thestereoscopic 3D video. The texture video and its per-pixeldepth map can provide a exible virtual view synthesis capa-bility. Since the depth map is easy to be compressed with alower bit-rate compared with the texture video, the video plusdepth format is suitable for the stereoscopic 3D video appli-cation.

With the high efciency 3D video coding technologies,the 3D video data amount is greatly reduced so that it is easyto be distributed over the bandwidth-limited mobile Internet.Since the current mobile Internet usually involves the het-erogeneous access networks, the heterogeneities in channelbandwidth and channel quality bring many great challengesfor wireless 3D video streaming. To deal with these chal-lenges, scalable video coding can be used for 3D video toprovide the channel-adaptive scalable view synthesis abilities[4].

For robust video transmission, the application layerpacket-level FEC [5] is generally used as a complement forthe physical layer channel coding. Though packet-level FECcan provide the stronger protection for the transmitted stream,the amount of redundancy rate has a great inuence on thetransmitted data amount. When the channel quality is good,the little FEC rates can be added; conversely, more FEC ratescan be used to cope with the high packet loss situation [6].Under the available channel bandwidth constraint, how to al-locate the FEC rate and source coding rate between the differ-ent layers [7] of video and depths to adapt to the packet lossstatus is an important optimization problem. Since the inwardvideo/depth rate allocation also has a great effect on the vir-tual view quality [8], the transmission rate allocation problemwill be more complicated when the FEC is involved.

In the past packet-level FEC research works, the impor-tance levels of the packets within one picture are assumed tobe equal and the specic video content characteristic is usual-

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ly not considered in the FEC rate assignment. For video plusdepth based 3D video, the stereoscopic 3D saliency can beused to assist the FEC rate allocation within the picture forpromoting the protection efciency. Since 3D visual saliency[9] involves the depth saliency which locates the comfortabledepth ranges interested by the viewer, the overall 3D saliencymap can be generated by integrating depth saliency map withthe 2D saliency map. Thus, the identied high 3D saliencyregion can be protected with more FEC rates to reach the goalof content-aware unequal error protection.

In this paper, we propose a novel joint video/depth/FECrate allocation method for scalable 3D video streaming, whichunequally allocates FEC rates for the scalable 3D video datalayers as well as video and depth with adapting to the trans-mission packet loss rate (PLR). With maximizing the 3D visu-al quality, the optimal bit-rate trade-off between video, depthand their FECs of different layers can be found. Based on theoptimal video/depth/FEC rate allocation, we further optimize

the FEC rate allocation between different regions within thepicture by utilizing the content-based 3D visual saliency.The rest of the paper is organized as follows. Section 2

provides the proposed joint video/depth/FEC rate allocationmethod for scalable 3D video streaming. Specically, therate-constraint video/depth/FEC rate allocation is rstly intro-duced and stereoscopic 3D saliency based FEC rate allocationwithin the picture is then interpreted. Section 3 presents theexperimental results and Section 4 concludes the paper.

2. JOINT VIDEO/DEPTH/FEC RATE ALLOCATION

2.1. Layer-based FEC for scalable 3D video streaming

To adapt to the time-varying bandwidths, the MGS (medi-um grain scalability)-based SNR (signal-to-noise ratio) scal-able video coding is separately used to encode the video anddepth. With the SNR layers of video and depth, as shownin Fig .1, there are many different combinations of video anddepth rates, which can be dynamically selected to adapt totime-varying networking bandwidth.

Texture Video

v R

Depth

VL1

VL2

VL3

VL4

DL1

DL2DL3

3 D R

d R

Fig. 1 . Rate combinations of scalable video and depth ( R v :video rate, R d : depth rate, R 3D : sum of video and depth rates,VL: video layer, DL: depth layer)

Besides the networking rate adaptation, the error re-silience is also a key problem in the 3D video streaming.To provide the packet-level protection, the FEC rate can al-so be added into the transmitted stream. The different FECrates can provide different protection efciencies with differ-ent transmitting overheads. For the FEC correction scheme

(we use RS codes), the k source packets are encoded inton packets with adding h = n k redundancy packets toguarantee the k original source packets can be successivelyrecovered only if a minimum of k packets are received. Forthe SNR scalable coded data, the different layers can be pro-tected with different FEC rates to provide the unequal protec-tions for different importance levels of layers. Fig. 2 showsthe packet-level layer-based FEC mechanism for 3D videostreaming. According to the rule of unequal error protection,a larger amount of redundancy packets can be added into thetexture stream since the texture video takes a more importantrole than depth in the virtual view synthesis.

1n

VL1 VL2 VL3

DL1 DL2 DL3

1h

1k

3k

2k

2h

3h

2n

3n

3n

2n

1n

1

k 1

h2

k 2

h3

k 3

h

Video

Depth

Fig. 2 . Packet-level layer-based FEC mechanism for videoand depth (3-layers)

2.2. Channel-adaptive joint video/depth/FEC rate alloca-tion

Based on the channel information feedback, we can dynam-ically select the video/depth/FEC rate to adapt to the trans-mission channel. Assume that there are M v layers of videodata and M d layers of depth data can be selected for transmit-ting. Under the total channel rate constraint R , we can selectdifferent FEC redundancy ratios to change FEC rate of video

R vFEC (

L v ) and FEC rate of depth R dFEC (

L d ) for obtainingdifferent 3D visual qualities, where

L v denotes the FEC rate

allocation vector for the selected video layers and

L d denotesthe FEC rate allocation vector for the selected depth layers.By integrating the FEC rate allocation with video/depth rateallocation, the optimal video/depth/FEC rate allocation can beexpressed as,

(R optv , Roptd , R

optvFEC (

Lopt

v ), RoptdFEC (

Lopt

d ))

= arg maxR v

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Lopt

v and

Lopt

d are the optimal

L v and

L d which can result inthe optimal 3D video quality.

In (1), the 3D visual quality is obtained by the coarse pool-ing of the video quality and depth quality, and in this work weadopt the simple pooling process suggested in [10] by substi-tuting depth map for disparity map as

Q 3D = a (Q v )d + b (Q d )e + c (Q v )d (Q d )e , (2)

where the video quality Q v and depth quality Q d need to beestimated before transmission, and ae are pooling modelparameters which can be obtained by an off-line training way.In our experiment, we set a = 2 .381, b = 0 .001, c = 0 .001,d = 2.127, e = 3 .821. This kind of video and depth qualitypooling can approximately reect the 3D visual quality, andits effectiveness has been conrmed in [10] with the extensivesubjective tests.

To obtain the overall 3D visual quality, the qualities of video and depth in terms of signal distortions are indepen-dently estimated through predicting their packet loss prob-abilities. Since the layer-based FEC is used, the packetloss rate for each layer must be rstly estimated. Based onthe Bernoulli error model, the packet loss rate for the m th

(m < M v for video and m < M v for depth) layer after theRS (N m , K m ) encoding without considering the layer depen-dency can be obtained by

m =K m

j =1 N m

j ()j (1 )N m j , (3)

where the packet error probability for each packet is equalwith that packet losses are identically distributed across al-l source data and FEC packets. What should be mentionedhere is that the packet error probability can be regarded as theaverage packet loss rate in the statistical sense.

Further, the packet loss rate m for that only the rst mlayers are decodable in the total M v layers for texture videocan be expressed as

m =

m m 1

j =1 (1 j ), 1 < m < M vm , m = 1M v

j =1(1 j ), m = M v .

(4)

Likewise, the packet loss rate m for depth can be comput-ed by substituting M d for M v in (4). With the estimatedPLRs for different layers, the total signal distortion for videoor depth can be further estimated by

D =M

m =1 m D m , (5)where D m denotes the video or depth distortion that only therst m layers are decodable, and M denotes the M v for video

distortion estimation and M d for depth distortion estimation.Finally, the overall Q 3D can be computed with (2) by usingthe PSNRs ( Q v and Q d ) of video and depth which are con-verted from their estimated signal distortions.

With equations from (1) to (5), the specic rate allocationcan be sequentially performed with group of pictures (GOP)unit. For each GOP, we monitor the transmission PLR , andthen obtain the optimal video/depth/FEC rate allocation resultthrough full searching all candidate rate allocation cases bymaximizing the 3D visual quality.

2.3. 3D saliency based FEC assignment

With the joint video/depth/FEC rate allocation, the optimalGOP-level rate combinations of video, depth and their FECrates can be selected. This optimal selection is based on theassumption that the importance levels of the packets in oneframe are equal. However, this is usually not the case for the

practical 3D video viewing. In the 3D video viewing, peo-ple usually pay more attention to the interesting parts of onepicture than the other parts. Hence, the 3D saliency charac-teristics can be considered into the FEC rate allocation withinthe picture to further enhance the FEC performance.

To deliver high 3D visual experience over bandwidth-limited channel, more FEC rates need to be allocated to somesaliency regions to which users pay attention. For saliency-based FEC rate allocation, the 3D visual saliency map shouldbe rstly generated. Unlike 2D visual saliency, 3D visualsaliency involves the interested depth range [9]. Therefore,we can estimate the 3D saliency by fusing the 2D saliency anddepth saliency. Though we can separately process the videoand depth to generate their corresponding salience maps andthen use them to assist the FEC rate allocation for video anddepth. However, this kind of FEC scheme with the individualsaliency maps can not provide the optimal overall 3D salien-cy in the nal 3D video viewing. The 2D saliency map anddepth saliency map need to be merged into one uniform 3Dsaliency map to guide the FEC rate assignment for video anddepth.

2D video saliencycomputation

Video

Depth saliencycomputation

Depth

Saliency map merging

2D saliency map Depth saliency map

3D saliency map

Fig. 3 . 3D saliency map generation

The completed 3D saliency map generation ow is shownin Fig. 3. The 2D visual saliency map is rstly extractedusing AIM model [11] which is based on a premise that lo-calized saliency computation serves to maximize informationsampled from ones environment. After that, depth-based vi-

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sual attention analysis is used to generate the depth saliencymap. The depth of the object often correlates with its saliencylevel. Generally, the captured scene depth is not totally con-formed to the perceived depth range on the target display, sothat some regions with appropriate depth ranges needs to begiven the high saliency to guarantee the perceived depth sen-sation [12]. The contents of foreground often gain more atten-tions of human being than those of background [13]. There-fore, we allocate the high saliency values to the regions closeto minimal value of the depth range and a low saliency to themaximal value of the depth range. Thus, the depth salien-cy map is generated through simply mapping the appropriatedepth range to saliency map.

Finally, we obtain the 3D saliency map S 3D by leveraging2D saliency map S v and depth saliency map S d using a linearmerging of them as

S 3D = (1 )S v + S d , (6)where is a leverage factor between 2D saliency and depthsaliency, which can be set to 0.5. Currently, how the 2Dsaliency and depth saliency are fused into the actual 3Dsaliency in the human brain is not very clear. However, thesimple linear weighting of 2D saliency and depth saliency canapproximately reect the actual 3D saliency. Fig. 4 shows the2D saliency map, depth saliency map, the merged 3D salien-cy map, and the corresponding segmented high 3D saliencyregion. It can be seen from Fig.4 that the merged 3D saliencymap basically reects the humans 3D saliency.

(a) (b) (c)

(d) (e) (f)

Fig. 4 . 3D saliency map for Balloons sequence. (a)Balloonsimage, (b)depth map, (c)2D saliency map, (d)depth salien-cy map, (e)merged 3D saliency map, (f) segmented high 3Dsaliency region

In the current stage, the 3D saliency map is generated inan off-line way before the 3D video streaming. The 3D salien-cy map is used as the complement data to assist 3D videostreaming. Given the 3D saliency map, more FEC rates canbe added into the high 3D saliency regions in each frame of video and depth.

3. EXPERIMENTAL RESULTS

To evaluate the proposed method, 3D video streaming withvideo/depth/FEC rate allocation is simulated in NS2. The

video and depth are independently encoded by the SVC soft-ware JSVM9.19 with 4 quality layers (1 CGS layer and 3MGS layers). To facilitate the concentrative protections of the dispersed saliency regions, exible macro-block coding(FMO) is used to encode the CGS layer of video and depth.In the experiments, view4 for Lovebirds1 with 200 frames(view5 is virtual view) and view1 of Balloons (view2 is virtu-al view) with 300 frames are used. To estimate the packet lossinduced signal distortion, Open SVC Decoder [14] with de-fault error concealment is used to decode the error-corruptedscalable 3D video stream.

For FEC coding, we use RS (10, k ) with dynamic k to reg-ulate the FEC redundancy rate. To illustrate the performanceof the proposed joint video/depth/FEC rate allocation method,xed ratio 5:1 video/depth rate allocation method with xedFEC redundancy rate ratio of 0.2, and joint video/depth/FECrate allocation without 3D saliency guidance are used as thecomparison solutions for the 3D video streaming.

Currently, for 3D saliency guided FEC rate allocation,we only add 3D saliency guided FEC into the CGS layer of video and depth since the FMO encoding of MGS layer is notwell supported in the JSVM9.19. Hence, the effectiveness of 3D saliency based video/depth/FEC rate allocation is veriedwith the CGS layer into which the layer-based rate allocationis not involved.

To verify the performance of the proposed join-t video/depth/FEC rate allocation method, subjective 3D qual-ity evaluation is performed with a 15.6 inch lenticular lensesbased stereoscopic display (TOSHIBA Qosmio F750 laptopcomputer). The subjects sat in front of the screen with com-

fortable viewing distance and the eld of view was about 15o

.The SSIS (Single Stimulus Impairment Scale) method de-scribed in ITU-R BT. 500 [15] and ve grade scales of MOSare used in the subjective tests. 15 subjects are involved in-to the subjective tests and the average MOS of their scores isused to evaluate the experimental result.

Fig. 5 shows the performance of the proposed join-t rate allocation method under the constant channel rate of 4500kbps with different networking PLRs. It can be seenfrom Fig. 5 that the subjective 3D visual quality of xed ratiovideo/depth/FEC rate allocation method gets worse rapidlywith increasing PLR, and comparably, the subjective qualityof the joint video/depth/FEC rate allocation gets worse slowlywith the increasing PLR. It suggests that the proposed methodcan restrain the trend of the 3D quality getting worse throughregulating the trade-off among the FEC rates, video rates anddepth rates. It is also conrmed by the objective quality eval-uation of the virtual view which is shown in Fig. 6. Thoughthe proposed rate allocation method is based on the subjective3D visual quality evaluation, the video and depth distortion-s are also indirectly used in the rate allocation optimization.Therefore, the PSNR value of the virtual view synthesized bythe corresponding reference texture video and depth can alsoreect the efciency of the proposed method.

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0 0.02 0.04 0.06 0.08 0.13

3.5

4

4.5

5

PLR

M O S

Balloons

Joint rate allocation

Fixed ratio rate allocation

(a)

0 0.02 0.04 0.06 0.08 0.12.5

3

3.5

4

4.5

PLR

M O S

Lovebirds1



(b)

Fig. 5 . The 3D perceptual quality comparisons betweenthe joint video/depth/FEC rate allocation, and the xed ratiovideo/depth/FEC rate allocation under the constant rate con-straint of 4500kbps with different networking PLRs

0 0.02 0.04 0.06 0.08 0.124

26

28

30

32

PLR

P S N R ( d B )

Balloons



(a)

0 0.02 0.04 0.06 0.08 0.120

22

24

26

28

PLR

P S N R ( d B )

Lovebirds1



(b)

Fig. 6 . Objective quality (PSNR value) of the synthesizedvirtual view video

The proposed joint rate allocation method dynamicallyregulates the FEC rate ratios to adapt to the transmissionpacket loss situations to improve the error-resilience perfor-mance. Fig. 7 shows the FEC rate ratio variations for videoand depth under the transmission conditions of time-varyingnetworking PLRs (dynamically varied from 0 to 0.1) and theconstant rate constraint of 4500kbps. The FEC rate in Fig. 7for video or depth includes the allocated rates of total selectedlayers of video or depth. The FEC rate ratio changes with thedynamic PLR in the temporal dimension. The FEC rate ratiovariations for video and depth are distinctly different. It indi-cates that the FEC rate regulation affects the balance betweenvideo and depth to achieve the optimal 3D visual quality.

Generally, the available channel bandwidth is often time-variable for video streaming. Fig. 8 shows the performanceof the proposed joint rate allocation method under the time-varying channel rate constraints from 3000kbps to 5000kbpswith different PLRs. It can be seen that the proposed methodexhibits the dynamic adaptations to the channel uctuationand PLR variation. The dynamic rate allocation betweenvideo and depth with balanced FEC rate assignment alwaysprovides the superior 3D visual quality under the uctuatedchannel conditions.

For the proposed rate allocation method, FEC rate canalso be unequally assigned by considering the 3D saliencywithin the picture. Fig. 9 shows the subjective quality com-parison between joint video/depth/FEC rate allocations with3D saliency guidance and without 3D saliency guidance un-der the constant rate constraint of 3500kbps (only the CGS

0 5 10 15 20 25 30 350

0.1

0.2

0.3

0.4

0.5

GOP number

F E C

r a

t e r a

t i o

Balloons

VideoDepth

Fig. 7 . FEC rate ratio variation with increasing GOP number

0 0.02 0.04 0.06 0.08 0.12.5

3

3.5

4

4.5

PLR

M O S

Balloons



(a)

0 0.02 0.04 0.06 0.08 0.12

2.5

3

3.5

4

PLR

M O S

Lovebirds1



(b)

Fig. 8 . The 3D perceptual quality comparisons between

the joint video/depth/FEC rate allocation, and the xed ratiovideo/depth/FEC rate allocation under time-varying channelrate constraints from 3000kbps to 5000kbps

layer are involved into rate allocation). From the gure, it canbe seen that the MOS values of 3D saliency-based rate allo-cation are a little higher than those without 3D saliency guid-ance. It veries that the 3D saliency based FEC can providethe stronger FEC protection for the higher saliency region sothat the received total 3D perceptual quality is much better.Fig.10 shows the red-cyan stereoscopic anaglyph 3D picturesof Balloons sequence at the 117 th frame under the transmis-

sion PLR of 5%. In the pictures, the region enclosed by theellipse is visually with higher quality stereopsis for 3D salien-cy based FEC rate allocation than that without 3D saliencyconsideration. Comparably, the FEC rate allocation withoutsaliency consideration can not provide the key protection forthe region enclosed by the ellipse and possibly result in thepacket loss. Consequently, the perceptual 3D quality of FECrate allocation without 3D saliency consideration is usuallyworse than that of 3D saliency-based unequal FEC rate allo-cation.

4. CONCLUSION

This paper presents a joint video/depth/FEC rate allocationmethod with 3D saliency guidance for scalable 3D videostreaming. By utilizing the end-to-end 3D perceptual qualityestimation, the optimal video rate, depth rate and the corre-sponding FEC rates can be optimally assigned. The unequalprotections between video and depth as well as different da-ta layers are performed. Further, with the 3D saliency anal-ysis, the unequal FEC rate assignment within the frame isalso utilized to improve the FEC performance for 3D videostreaming. Experimental results showed that the proposed 3Dsaliency guided joint video/depth/FEC rate allocation method

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0.03 0.05 0.102.5

3

3.5

4

4.5

5

PLR

M O S

Balloons

With 3D saliencyWithout 3D saliency

(a)

0.03 0.05 0.102.5

3

3.5

4

4.5

5

PLR

M O S

Lovebirds1

With 3D saliencyWithout 3D saliency

(b)

Fig. 9 . The 3D visual quality comparison between the jointvideo/depth/FEC rate allocations with and without 3D salien-cy guidance (95% condence interval)

can provide good error resilience performance for salable 3Dvideo streaming, and correspondingly achieve the higher per-ceptual 3D quality than the xed ratio video/depth/FEC rateallocation method.

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Fig. 10 . Stereoscopic anaglyph (red-cyan) comparison be-tween the FEC rate allocations with 3D saliency guidance andwithout 3D saliency guidance

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