SNR Scalable Transcoding for Video over Wireless Channels

SNR Scalable Transcoding for Video over Wireless Channels Yue Yu Chang Wen Chen Department of Electrical Engineering University of Missouri-Columbia Columbia, MO 65211 Email: { yyu,cchen} @ee.missouri.edu Absfracf- n this paper, we propose a novel scheme integrating transcoding, SNR scalability and error protection channel coding for video transmission over wireless channels. The target application for such error-resilient transcoding is to provide robust access to the pre-encoded high quality video server from mobile wireless terminals. The high bit rate of pre-encoded video sequence is reduced through transcoding to fit greatly Limited bandwidth of wireless links. The proposed transcoding is able to reduce both size of the video frames and the bit rate of the preencoded video. We also design the transcoder such that the transcoding output bitstream has the desired SNR scalability in order to guarantee a basic quality of video transmission under the hostile wireless transmission environment. With SNR scalable bitstream output, we are able to incorporate unequal error protection through channel coding for the base layer and the enhancement layers, respectively. Different rate of CRC/RCPC is adopted so as to simplify the implementation of a single channel decoder at the receiver. Experimental results show that the proposed integrated scheme of SNR scalable transcoding can guarantee the basic decoded visual quality under random and bnrsty error channel conditions and outperform the conventional single layer transcoding without channel coding. Keywords- SNR Scalability, tanscoding, Video, MPEG, Wireless Channel. NTRODUCTON Emerging networked multimedia applications involve a diverse access links, including wired networks, ATM, wireless local area networks (LAN), satellite networks, and so on. Such heterogeneous network links are able to provide a wide variety of services to users with different types of access terminals. A typical example of multimedia access across the wired and the wireless networks is shown in Figure 1. Because of the significant difference between these two networks, the key component to ensure a successful access to the multimedia server in the wired network from a mobile wireless user is the design of the gateway or proxy server that sits at the boundary between these two networks. n the case of video transmission, the proxy server at the boundary of wired and wireless networks shall be able to convert the high quality video that has been pre-encoded to a low bit rate video to be successfully delivered to the mobile wireless user. For example, a high quality DVD video program existing in a video server cannot be delivered to a mobile user in real time if video bitstream is not transcoded to lower bit rate to fit the bandwidth constraint. n addition, an error-resilient capability of the transcoded bitstream is very much desired since the wireless channels are usually error-prone. This research is supported by University of Missouri Research Board Grant URB-98-142. : Mobile User - L - - - - - _ - - - - - - - - - - - Fig. 1, The illustration of heterogeneous network. A challenging problem in designing such a proxy server is the integration of both transcoding and error-resilience into a single signal processing unit. n general, the desired proxy server shall be able to accomplish the following: (1) choosing the type of services, (2) shaping the compressed bitstream to desired bit rate and (3) adding appropriate error protection to the transcoded bitstream. n this research, we target the application of delivering high quality MPEG-2 compressed video sequence to a mobile user. The integrated transcoding unit is expected to convert the original high bit rate and wireless unaware MPEG-2 compressed video sequence to low bit rate and error-resilient bitstream for wireless LAN access. Existing transcoders can be generally divided into two categories. One category is to decode the pre-encoded bitstream to the pixel domain first [l],[2], and then re-encode it at a desired bit rate. This type of transcoding should be optimal from the PSNR point of view. However, the corresponding computational expense of such transcoder is high because of the need for complete decoding and encoding processes. Another category of transcoders [3], [4], [5] is to decode the bitstream only to the DCT domain to requantize the coefficients. Then, the reduced bit rate bitstream is generated using the existing motion vector (MV) and the requantized coefficients. This type of transcoder results in significant reduction in computational needs, but the coding performance may be degraded. These existing methods have been developed for the applications with reduced bandwidth only. No consideration in errorresilience has been incorporated into these schemes. Therefore, conventional transcoding schemes cannot be adopted for the proxy server for wireless access applications. Without 0-7803-6596-8/00/$10.00 0 2000 EEE 1398

error-resilience, the decoder at the mobile receiver will most likely break down when decoder is fed with a bitstream corrupted by the channel noise in the wireless link. n this paper, we propose an integrated scheme that adopts a cascade architecture to combine the transcoding, SNR scalability, and channel coding, into a stream of operations to convert a high bit rate video bitstream into a desired bit rate video for wireless access. Section 1 describes in detail the overall structure of the proposed system and the main components of the proposed proxy server. Section 11 presents the experimental results based on a typical MPEG-2 video sequence and a wellknown wireless channel model. Section V concludes this paper with a summary and some discussion. 11. SYSTEM DESCRPTON The main goal of the proposed research is to design a transcoding scheme that would convert a high bit rate video bitstream to a low bit rate and error resilient video bitstream for wireless access applications. Although there are many existing error resilient techniques [6] developed for various applications, most of them are inappropriate for integration with transcoding application. Many of them [7] do not comply with the current international standards, including MPEG- 1,2, H.26 1, H.263. Most of them are unable to guarantee the basic decoded quality under noisy channels, since the problem of error propagation has not been effectively resolved. n the case of bursty channel errors, the error corrupted bitstream cannot even be decoded. One way of preventing the channel error from propagating has been developed in [S which attempted to change the distribution of intra coded blocks. However, when the channel BER becomes high, this scheme is still unable to prevent the error propagation and would result in very poor visual quality for the reconstructed video frames. n the section, we will first present the overall system of the proposed error resilient transcoding. We will then explain why we choose to adopt the SNR scalable transcoding and corresponding unequal error protection. A. Overall System Diagram The proposed SNR scalable error resilient transcoding system is shown in Figure 2. First, the pre-encoded high rate bitstream is decoded to the pixel domain. This is necessary as we need to implement spatial downsampling because of the limited display capabilities of wireless devices. Then, we implement SNR scalable transcoding that generates two layers of bitstream to a suitable bit rate for the desired application. For these two different priority bitstreams, different rate CRCRCPC channel coding is employed to combat the mobile channel noise. B. SNR Scalable Transcoding Generally, the structure of the adopted transcoding scheme will be dependent upon a specific application. n the case of developing error-resilient transcoding scheme for the interface nputs!j+m Mobile user SNR scalable banding 4 ChannelCoding > between wired and wireless networks, we need to consider both the limited bandwidth and the implementation complexity. To access the high fidelity pre-encoded video in the video server from a mobile wireless terminal, an operation on such pre-encoded video bitstream to convert the bit rate video bitstream to an appropriate bit rate is needed in order to satisfy the bandwidth constraint. For MPEG-2 video, it is necessary to convert the pre-encoded video bitstream at several Mbps to the transcoded video bitstream at several hundred Kbps. n addition, due to the limitation in display capacity, frame size needs to be reduced. Such reduction in frame size is also beneficial to the visual quality of the transcoded video since a high reduction in bit rate may result in unacceptable visual quality degradation if the frame size is not reduced accordingly. n this research, the D1 size of the pre-encoded MPEG-2 video sequence will be subsampled to QCF size for delivery to a mobile wireless terminal. Because of the reduction in size for the transcoding of the video frames, it will be very difficult to implement the transcoding of the pre-encoded bitstream in the frequency domain, even though the computational expense in this case is generally low. Since the desired transcoding output is in QCF format, the computational expense to re-encode such a size of video sequence is acceptable. Therefore, we propose a transcoding scheme that first completely decode the video frames to their pixel domain, subsample the decoded video frames to the size of QCF, and re-encode the QCF video frames to a desired bit rate to meet the bandwidth constraints. Scalable video coding is very much desired for the delivery of coded video bitstream over error-prone channels. Scalable coding provides an opportunity for the transcoding design to incorporate unequal error protection so that the basic layer can be appropriately protected in order to guarantee the basic visual quality of the received video frames at the receiving end. The enhancement layers are protected less so that the overall bit rate can still satisfy the bandwidth constraint. n the case of highly noisy channels, we assume the base layer can be fully recovered after channel decoding while the enhancement may not be. f the enhancement layers can also be decoded properly, the basic video quality will be enhanced with additional information from the enhancement layers and results in a better visual quality of the received video. f not, the received base layer bitstream is still adequate for the decoding of video frames with basic visual quality. There are four types of scalable techniques [9] for video coding, including data partitioning, SNR scalability, spatial scal- 1399

ability and temporal scalability. These scalable video coding have been adopted by some existing international video standards [ 101 as an option. We adopt the SNR scalable coding for the transcoding design since this type of scalability has a direct relationship with the quality of the reconstructed video frames [9]. Furthermore, this type of scalability requires reasonable overhead bits while provides good performance improvement. Generally, SNR scalability puts all the motion-vector, motion compensation mode information and coarse quantized DCT coefficients in the base layer. The enhancement layer will consist of mainly finer quantized difference between the DCT coefficients of the original image and the DCT coefficients of the reconstructed base layer. Because of the error propagation characteristics in the compressed video, a few errors may cause the decoder to break-down. Therefore, high performance channel encoding is needed to guarantee that the base layer is transmitted error free. Evidently, an important research topic in error resilient SNR scalable codec design is how to allocate the limited bit budget between base layer and enhancement layers so that the best quality of the transmitted video can be achieved. However, because of the involvement of variable length coding (VLC) in most video coding standard, an analytic solution cannot be easily derived. n this research, we will try to allocate the bit budget according to the given channel BER. The proposed error resilient SNR scalable transcoding scheme is shown in Figure 3. Decoded frame > Subsampling size to QClF SNR scalable transcoding C. Channel Coding Fig. 3. The SNR scalable transcoding. ntuitively, channel coding is applied to combat the noise in the communication system by adding controlled redundancy. Current protocol for wireless access is designed for either data transport or voice communication, the channel coding strategy inherent in the wireless communication system is inappropriate or inadequate for reliable video transmission. Traditional channel coding would treat the compressed bitstream as pure data and would not recognize the different importance in different part of the compressed video bitstream. For the scalable bitstream, it is obvious that the base layer is more important than the enhancement layer. So, an appropriate channel coding should allocate more channel coding bit budget to the base layer. However, since the wireless channel varies with time and the compressed bitstream is generated with VLC coding, the problem of rate allocation between the base layer and the enhancement layers cannot be analytically resolved. Similarly, it is also impractical to derive an explicit expression for bit allocation between source coding and channel coding so as to achieve the end-to-end minimum distortion in visual quality. > n this research, we will investigate CRCRCPC channel coding [ll] that has been employed for still image transmission over noisy channel [ 121. The advantage of CRC channel coding is its low computational complexity. The RCPC coding was originally introduced by Hagenauer [ 1 ] et al as an extension to the punctured convolutional code for the purpose of obtaining simpler Viterbi decoding for rate KN codes. The main reason for us to choose such channel coding in this research is that it is easy to facilitate different coding rates to an input bitstream using only one encodeddecoder pair. Comparing with the punctured convolutional codes, RCPC coding has an additional constraint that the puncturing of the convolutional code follows a rate-compatibility rule. This implies that all the coded bits of a high rate punctured codes are embedded into the lower rate codes of the same family. Since the codes are compatible, rate variation within a data frame is possible to achieve the desired unequal error protection for robust video transmission. 111. EXPERMENTAL RESULTS We have conducted some experiments to verify the performance of the proposed error-resilient transcoding schemes. n this section, we will first describe the channel models and then present the experimental results. A. Channel Models n this research, both memoryless channels and bursty channels are considered. More specifically, binary symmetric channels (BSC) are employed to model the memoryless channels and Gilbert Elliott channels (GEC) are employed to model the bursty channels. For the BSC model, the given random bit error rate (BER) is the only parameter to describe the channel condition. For the GEC model, it is a two-state model that is able to simulate the wireless channel fading effects. n the GEC model, one state represents the good (G) condition and the other represents the Bad or Burst (B) condition. As Figure 4 shows, P, Q represent the transition probabilities from one state to the other while 1 - P, 1 - Q are probabilities of remaining in state G or B. For each state, it is a BSC model with a given BER EG and EB. P Q Fig, 4. The Gilbert Elliott Channels Model. The mean bit error probability BER generated by this channel model is P x EB + Q x EG BER = 1-P (1) 1400

where p = 1 - (P + &) is the correlation of the bit errors. t is also a measure of the bursty or random characteristics of the channel. p = 0 implies that the channel error is nearly random while p = 1 implies that the channel is extremely bursty. B. Preliminary Results n this research, an MPEG-2 [ 101 video sequence is used to test the proposed scheme. Pre-encoded video sequence is generated at a high bit rate 6.5Mbps. The experimental sequence consists of 29 frames of Susie. The transcoding encoder also adopts MPEG-2 structure while the size of frame is subsampled to QCF. The related parameters of the encoder are: (1) Bit rate: 640 Kbps, (2) Length of GOP: 12, and (3) Distance between two P frames: 3. The total available bandwidth for the wireless channel is assumed to be 64OKbps. We would allocate 384Kbps to the base layer and the remaining 256Kbps to the enhancement layer. Two different RCPC rates for the base and enhancement layers are 315 and 415, respectively. The corresponding bit rates for the source and channel coding are: 230.4 Kbps and 153.6 Kbps, respectively, for the base layer, and, 204.8 Kbps and 51.2 Kbps, respectively, for the enhancement layer. The first experiment is to study the reconstruction of the received video bitstream generated by directly transcoding the pre-encoded bit sequence to one layer with 640Kbps and then transfer this transcoding output at the desired channel without SNR scalability and channel coding. The second experiment is study the reconstruction of the received video bitstream generated by transcoding the pre-encoded bitstream to SNR scalable two layers, but without the channel coding. n this case, the bit rates for source coding of base and enhancement layers are 384Kbps and 256Kbps, respectively. The third experiment is study the reconstruction of the received video bitstream generated by transcoding the pre-encoded bit stream into the SNR scalable two layers with above appropriate channel coding rate. The resultant PSNR values of all three experiments are obtained based on 30 simulations of the given channel parameters. Since there is no error concealment employed at the decoding end, an uncorrected channel error may result in the breakdown of the decoder. Therefore, for experiments 1 and 2, we applied minimum channel coding so that the received bitstream is just decodable. Figure 5 shows the average PSNR of the decoded video sequence of above three experiments over BSC channel when the channel BER is 5 x From this figure, we can see that the proposed scheme performs better than transcoding without error-resilience considerations, even though we have applied minimum channel coding to these transcoded bitstream. This is true even when only base layer is received at the receiving end. When the enhancement layer is also received, the video quality is much improved. The temporal average PSNRs over 29 frames for the four curves shown in Figure 5 are 34.52 db, 31.91 db, 30.94 db, and 27.38 db, respectively. On average the video quality improvement by adding enhance- ment layer is about 2.5 db. However, if we directly transcode the pre-encoded bitstream into one layer at 640Kbps without channel coding, the decoded performance can be very poor because of channel error corruption. The average PSNR difference is more than 7 db. The results from transcoding the output as SNR scalable two layers without channel coding are also shown in the figure for reference. The SNR scalable coding improves the error resilience. However, further improvement can be observed with appropriate channel coding. An interesting phenomena we can observe from this figure is that there are three peaks at frames 1, 13, and 25. This is because these frames are frames which are not affected by the error propagation from B or P frames and therefore generally have higher PSNR values. n particular, if the frames are not severely corrupted by the channel error, the PSNR of the these frames can be relatively high even without channel coding. 2s t 1 2o t 5 10 15 20 26 30 F,.. fig. 5. PSNR of the decoded video sequence over BSC channels. BER=5 x Solid line: The proposedmethod; Dash line: Base layer only for the proposed method; Dashdot line: lko-layer transcoding without channel coding; Dotted line: Single layer transcoding without channel coding. Figure 6 shows the case of GEC channel model when BER is 5 x We have observed a similar results as in the case of BSC channels. However, because of the bursty type of error, the average PSM of the decoded video sequence has been further degraded. The temporal average PSNRs over 29 frames for the four curves shown in Figure 6 are 30.64 db, 29.00 db, 27.00 db, and 23.08 db, respectively. For all three experiments, the degradations due to bursty error are in the range of 3-4 dbs. As wireless channels exhibit bursty type of channel error, the GEC model-based experiments more realistically reflect the performance measure of the proposed scheme. Again, there are three peaks at frames 1, 13, and 25, because these 1 frames are not affected by the error propagation from B or P frames. Figure 7 shows the performance degradations due to bursty channel noise for the single layer transcoding without channel coding scheme as well as for the proposed error-resilient transcoding scheme. The temporal average PSNRs over 29 frames for the four curves shown in Figure 7 are 37.14 db, 23.08 db, 35.65 db, and 30.64 db, respectively. t is clear that, under noise free transmission environments, the PSNR of the single layer transcoding is higher than the proposed scheme, 1401

(5 t 2o 15 s 10 5 20 25 30 F,. Fig. 6. PSNR of the decoded video sequence over GEC channels. BER=5 x Solid line: The proposedmethod Dash line: Base layer only for the proposed method Dashdot line: Avo-layer transcoding without channel coding; Dotted line: Single layer transcoding without channel coding. since the proposed scheme allocate some bit budget for channel coding. However, under GEC noisy channel environment, the degradation of the the single layer transcoding is very significant, dropping an average of 14 dbs in PSNR. Moreover, the PSNRs of most frames are in the range of < 25 db, and are becoming visually annoying. For the proposed scheme, the degradation in around 5 db, and most frames have a PSNR value around 30 db, and therefore are visually pleasant. 15 15;,...... 5 5 20 25 30 F. Fig. 7. The comparison of PSNR of the decoded video at noise free and noisy environments. Solid Line: Single layer without channel coding - noise free; Dash line: Single layer without channel coding - GEC channels with BER=5 x Dashdot line: The proposed method - noise free; Dotted line: The proposed method - GEC channels with BER=5 x v. CONCLUSON AND DSCUSSON n this research, we proposed a scheme by integrating the transcoding, SNR scalability and channel coding to facilitate the error resilient delivery of pre-encoded video streams existing in the video server over the wireless channels. Through transcoding, both the size of video frame and the bit rate are reduced, and the bit rate of pre-encoded bitstreams are adapted to the bandwidth of wireless channels. SNR scalable encoding is employed to provide the scalable structure so that unequal error protection can be implemented. CRC/RCPC provides the different rate channel coding for base and enhancement layers so that the base layer can be transferred nearly noise free. Experimental results based on BSC and GEC channels show, \ that the proposed scheme can guarantee a basic decoded visual quality and results in an acceptable degradation in PSNR comparing with transmission in noise free environment. While the optimal bit rate allocation among source, channel, base and enhancement layer is an unsolved research, our experimental results show that the proposed heuristic solution is able to provide a practical solution to video over wireless channels. REFERENCES J. Youn, M.T. Sun, and C.W. Lin, Motion vector refinement for high-performance transcoding, EEE Trans. on Multimedia, vol. 1, No. 1, pp. 30-40, March 1999. H. Sun, W. Kwok, and J.W. Zdepski, Architecture for MPEG compressed bitstream scaling, EEE Trans. on Circuits and Systems for Video Technology, vol. 6. No. 2, pp. 191-199, April 1996. P. A. A. Assuncao and M. Ghanbari, A frequencydomain video transcoder for dynamic bit-rate reduction of MPEG-2 bit streams, EEE Trans. on Circuits and Systems for Video Technology, vol. 8, No. 8, pp. 953-967, Dec. 1998. B.Shen,.K.Sethi, and B. Vasudev, Adaptive motionvector resampling for compressed video downscaling, EEE Trans. on Circuits and Systems for Video Technology, vol. 9, No.6, pp. 929-936, September 1999. O.Wemer, Requantization for transcoding of MPEG-2 intraframes, EEE Trans. on mage Processing, vol. 8, No.2, pp. 179-191, February 1999. Y. Wang and Q. E Zhu, Error control and concealment for video communication : A review, Proc. of EEE, vol. 86, No. 5, pp. 974-997, May 1998. C. S. Kim, R. C. Kim, and S. U. Lee, Robust transmission of video sequence over noisy channel using paritycheck motion vector, EEE Trans. on Circuits and Systems for Video Technology, vol. 9, No. 7, pp. 1063-1074, October 1999. G.D.Reyes, A.R.Reibman, J.C.Chuang, and S.F.Chang, Video transcoding for resilience in wireless channels, EEE nternational conference on mage Processing, October 1998. R.Aravind, M.R.Civanlar, and A.R.Reibman, Packet loss resilience of MPEG-2 scalable video coding algorithms, EEE Trans. on Circuits and System for Video Technology, vol. 6, No.5, pp. 426-435, October 1996. SO/EC 13818-2, Generic coding of moving pictures and associated audio information:video, 1995. J. Hagenauer, Rate-compatible punctured convolutional codes and their applications, EEE Trans. on Communication, vol. 36, No. 1, pp. 389-400, April 1988. [ 121 P. G. Sherwood and K. Zeger, Progressive image coding for noisy channels, EEE Signal Processing Letters, vol. 4, NO. 7, pp. 189-191, July 1997. 1402