Unidirectional Encoder Rate Control Scheme for Transform Domain

2012 IEEE International Conference on Multimedia and Expo Workshops

Unidirectional Encoder Rate Control Scheme for Transform Domain Distributed Video Coding Vijay Kumar, Somnath Sengupta Electronics & Electrical Communication Engineering Indian Institute of Technology Kharagpur India-721302 Email: [email protected], [email protected]

In Tonomura et.al’s scheme [5], gray codes are employed to convert the binary to gray at encoder. At the decoder, parallel decoding is employed, resulting in a reduction of decoding time. In a recent scheme proposed by the Huynh et.al [6], multiple Low Density Parity Check Accumulate (LDPCA) decoding scheme is employed for decoding the bit planes in each band by running the LDPCA decoders in parallel. The inter bit plane correlation is exploited while decoding the bit planes. Instead of keeping the SI fixed throughout the decoding process of a frame, SI refinements may be employed to improve the SI along the decoding of the frame [7, 8, 9]. Apart from the estimation of SI and Correlation Noise (CN), mode decision is one of the important factors to improve the Rate-Distortion (RD) performance, where decoderdriven Skip and intra modes are employed in addition to the WZ mode for WZ frames [10, 11]. As the SI is available at the decoder and the original frame is available at the encoder, accurate estimation of bits to decode the WZ frame is apparently contradictory. The ’request and decode’ approach makes the decoder complex, introduces delays and makes the presence of feedback channel essential. Accurate estimation of number of bits not only makes feedback channel redundant and decreases the decoder complexity, but also aids in the accurate mode decisions process [10, 11] to improve the RD performance. In some cases, feedback channel may not be available, forcing the encoder to estimate the number of bits required for decoding the frame, without having sufficient information about the generation of SI at the decoder, as the encoder is complexity constrained. As the exact estimation of number of bits for each bit plane is a difficult task in the ERC, errors in the bit planes will be present in case of any underestimation of number of bits. In such cases, the decoder has to carefully deal with it to minimize the residual errors in the decoded WZ frames. In order to overcome the underestimation pitfalls, most of the encoder rate control schemes employ an over-estimation of the bits, leading to reduction in coding efficiency. In [12, 13, 14], ERC schemes were proposed in the pixel domain. A better encoder rate control scheme in transform

Abstract—This paper proposes a unidirectional encoder rate control (ERC) scheme in the interpolation based distributed video coding. As the encoder is complexity constrained, accurate estimation of number of bits to decode each bit plane is indeed difficult at the encoder. In case of under-estimation of the bits, correction of the errors in the decoded bit planes, by utilizing the available information, is one of the important tasks at the decoder. This was addressed by recent schemes. In this paper, we present an improved ERC, considering higher group of pictures(GOP). The contributions of the proposed scheme are (1) adaptive rate estimation, considering the dependency across Wyner-Ziv frames (2) motion adaptive reconstruction and (3) Side information refinement after decoding all the frames in the GOP. The proposed scheme is tested with several sequences, showing improvements in the case of GOP-4. Keywords-Distributed video coding, Encoder rate control, Side information.

I. I NTRODUCTION Distributed video coding(DVC) shifts the computational complexity from the encoder to the decoder and finds applications in mobile communications, wireless video surveillance and wireless sensor networks. It is based on a fundamental result of information theory from Slepian and Wolf [1], later extended by Wyner and Ziv (WZ) [2].Most of the DVC schemes are based on the frame based approach, where the sequence is split in to key and non-key frames. The key frames are intra coded using the conventional coding scheme and the non-key frames are WZ coded. The WZ frames are estimated at the decoder by creating the side information (SI) using the previous decoded frames and errors in the SI are corrected using parity bits from the encoder. DISCOVER [3] is a popular DVC scheme, where advanced SI generation techniques are used by the motion compensated interpolation technique (MCTI). The bit plane decoding is carried out from Most Significant Bit (MSB) to Least Significant Bit (LSB) in the order. Each decoding bit plane uses the previous decoded bit plane information to refine the soft information required for decoding the bit plane. To enhance the bit plane decoding performance, different techniques are reported in the literature. Vatis et.al [4] employed the inverse decoding order from the LSB to MSB. 978-0-7695-4729-9/12 $26.00 © 2012 IEEE DOI 10.1109/ICMEW.2012.23

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domain for group of pictures (GOP)-2 is reported in [15], and improved in [16] by Brites et al.. The codec provides a promising result of equaling the RD performance of the DISCOVER codec. At the encoder, SI is generated by the Fast motion compensated interpolation (FMCI) and the parity bits for each bit plane is estimated by considering the inter bit plane correlation and the probability of error, in order to have more accurate estimation. Gray codes are used to increase the correlation between the bit planes. At the decoder, Probability Update Table (PUT) [17] is employed to correct some of the errors in the bit planes. After decoding each bit plane up to a certain number of iterations, if there are errors, the decoding of the bit plane is repeated by updating the soft input of the most likely bit which is having the error based on the output Log Likelihood Ratio (LLR) of the channel decoder. If the bit plane is not decoded successfully, then the previous LLR is used as the final LLR. After decoding the bit planes, soft reconstruction is employed using the final LLR. Using the partial decoded WZ frame and key frames, SI and CN refinement techniques are employed and the erroneously decoded bit planes are redecoded. Deligiannis et al. [18] proposed ERC codecs without the use of the feedback channel by using the SI dependent CN modeling. The scheme looks simple and provides a better performance. In the literature, different architectures for improving the RD performance of the feedback channel based DVC by improving the SI, CN and mode decision are reported. Despite the improvements in the feedback channel case, improving the performance of ERC scheme is one of the challenging tasks that for higher GOP’s. The existing ERC schemes [16, 17] consider the simplest case of GOP-2. In higher GOP (𝐺𝑂𝑃 > 2), there exist more levels of hierarchy, so estimation is tougher and under-estimation of bits has its adverse effects on the subsequent WZ frames. In this paper, we propose an ERC scheme for GOP-4 as shown in fig.1. The scheme includes the adaptive selection of rate estimation models at the encoder by considering the DCT-band and WZ frame position within GOP and error correcting tools at different stages of the decoder. To increase the bit plane decoding performance, iterative decoding by exploiting the inter bit plane correlation is employed. The soft input of the each bit plane is updated by considering the decoded information of all the remaining bit planes. The PUT [17] is applied independently on the bit planes and is able to correct some of the bit plane errors. However, its effect on the remaining bit planes were not considered. In this work, we employed the PUT on multiple bit planes and adopted iterative decoding to enhance the error correcting performance. In order to reduce the negative effect of the under-estimation case on the low motion blocks, we employed a motion adaptive reconstruction scheme. Finally, after decoding all the WZ frames in the GOP, SI refinement

scheme is employed, using the nearest neighbor decoded frames for correcting the remaining bit plane errors. All the above tools are integrated under the proposed scheme. The remaining paper is organized as follows. Section II presents the proposed scheme. Experimental results are shown in section III and section IV concludes the paper. II. PROPOSED SCHEME The detailed description of the proposed codec is explained below. A. Encoder At the encoder, the sequence is divided into key and WZ frames. For every GOP of size K frames, there is one key frame, which is encoded as conventional intra coding, and the remaining (K-1) frames are encoded as WZ. Each WZ frame is divided into 4x4 blocks, DCT is computed and the coefficients corresponding to the same band are grouped and quantized using a uniform quantizer. 1) Estimation of parity bits: At the encoder, SI is generated by the FMCI [16], which adds to the complexity at the encoder, but better rate allocation can be achieved. As the size of GOP increases, the estimation of the bits becomes quite difficult due to the increase in the temporal distance between the reference frames. In order to have a better rate estimation, the reference frames are down sampled by a factor r and is used for estimating the SI. r is the temporal distance between the current and the reference frames. For the SI estimation of the current frame in layer 𝐿0 , motion estimation is carried out using the 2x2 down sampled original frames of 𝐾1 and 𝐾2 , so that the search range used for the motion estimation can be increased, without increasing the encoder complexity, for generating the better SI. For the frames in layer 𝐿1 , the original spatial resolution frame is used for generating the SI. The rate estimation at the encoder is based on the models proposed in two recent approaches [17, 16] as shown in Eq. 1 and 2. In the former, the rate is over-estimated compared to the latter and provides the flexibility in the rate allocation by changing the weight factor w. 𝑅𝑏 =

1 √ 𝐻𝑏 × 𝑒 𝐻 𝑏 + 𝑤 × 𝑝 2 𝑅𝑏 =

(1)

√ 𝑝 × 𝑒𝐻 𝑏

(2) 𝑡ℎ

In the above equations, 𝐻𝑏 stands for the 𝑏 bit plane conditional entropy, given the corresponding SI; p is the relative error probability of the bit plane and w is a weighting factor. The rate estimation, as in eq.1, is employed in [17], without using the refinement tools at the decoder. The modified rate estimation given in eq. 2 is used in [16], by adding the refinement tools at the decoder. The above rate estimation schemes are applied for the case of GOP-2. As the size of the GOP increases, it is difficult to estimate

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Figure 1.

Proposed architecture

the accurate rate for the frames having relatively larger temporal distance between the current and reference frames. Any under-estimation of the parity bits leads to erroneous decoding of the bit planes, causing degradation in the quality of the reconstructed frame. At the decoder, the initial SI is refined by the refinement of the motion vectors, using the reconstructed frame and the erroneous bit planes are redecoded with the updated SI. The quality of the refined SI is dependent on the quality of the decoded frame used in the SI refinement process. In this case, the SI refinement techniques using the reconstructed frame with error bands may provide wrong motion vectors and degrade the quality of the refined SI. The effect will be less in the less GOP-2 [16] but its effect will be more pronounced for the higher GOP’s.

Figure 2.

model, which offers a better performance in the case of GOP-4, as shown in our results. Rate adaptation is carried out for the DC band, as it packs most of the energy within the DCT block. In our approach, the rate for the DC band for the frame in 𝐿0 is slightly over-estimated for the slices having high motion, to make sure that the DC band is most probably decoded, irrespective of the remaining band condition. Hence, employing the SI refinement techniques can provide better SI for correcting the errors in the remaining bands. Instead of directly sending the DC band as the hash [20], the proposed system encodes all the bands in the WZ mode. Due to the unknown error characteristics of each bit plane, it is difficult to make adaptive rate estimation at the bit plane level instead. Instead, we adopted it at the band level in our scheme. For the frame in 𝐿0 , Mean of Absolute Difference (MAD) between the estimated SI and the original frame is calculated. If the MAD is greater than a threshold (𝜃1 ), then the rate estimation is done using the eq. 1 for the DC band. Else, the rate estimation is based on eq. 2. For the remaining bands, we employ the rate estimation, using the eq. 2. The use of MAD discriminates between the high and the low motion slices and selects different rate estimation technique to have better RD performance. For the frames in 𝐿1 , we followed the rate estimation of eq. 2 which is a finer estimation as the 𝐿1 frames are non reference frames. Also, the reference frames for these are less distant, compared to the reference frames used for the frames in layer 𝐿1 . The currently encoded WZ frame is divided into regions of size (72x88). The encoded bit plane from the each region is organized to form a bit plane of length 396 bits and encoded using the LDPCA [19] codes.

Coding structure in interpolation based DVC GOP-4

Under-estimation of bits for the WZ frame in layer 𝐿0 leads to the degraded quality compared to the achievable quality with the exact estimation in the ideal sense. Drift error has its impact on the subsequent decoded WZ frames in layer 𝐿1 , which uses the WZ frame in layer 𝐿0 as the reference. The proposed scheme attempts to reduce the above problem by the adaptive selection of rate allocation

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B. Decoder

2) Iterative decoding with PUT: In the scheme [17], PUT is used to correct the bit plane errors based on the output LLR of the decoded bit plane by applying it independently for each bit plane. In DVC, the error correcting capability of the PUT can be further enhanced by exploiting the inter bit plane correlation in each decoding band. In our work, this was exploited, by using the PUT within the multiple LDPCA iterative decoding. For the most likely error bit in each bit plane, PUT is used and the bit planes are re-decoded by using the above procedure. If any bit plane is decoded successfully, then the next most likely error bit is selected and the procedure is repeated. This process is repeated for all the correctly decoded bit planes obtained using the PUT. 3) Refinement of Side Information and Correlation Noise: In ERC, the bands will be erroneously decoded, if there is under-estimation. In the scheme [16], soft reconstruction is employed to combat the residual errors on the final decoded frame and the reconstruction frame is generated by using all the decoded bands (error + error free). As the reconstructed frame is used for the SI refinement, use of the erroneously decoded bands in the reconstruction sometimes misleads the motion estimation process by providing false motion vectors, resulting in poor quality of the refined SI, as compared to the previous generated SI. In order to have a better refined SI with better quality reconstructed frame, the reconstructed bands are generated using the error free decoded bit planes by using the minimum mean square error (MMSE) [3] reconstruction step for each DCT coefficient and afterwards, IDCT is used to get the reconstructed frame. After generating the refined SI and CN as in [16], the erroneous bands are once again decoded with the refined one to correct the errors in the bit planes. After decoding all the bands or after reaching certain number of SI refinement iterations, motion adaptive reconstruction is employed to get the decoded coefficients. 4) Motion adaptive reconstruction: Let us consider the SI and the original coefficient of 𝑏𝑡ℎ band for a particular 4x4 block as Y and X respectively. In general, there exists cases, where the error between the X and Y is less but may be located in the different bins. In feedback based DVC schemes, decoding of the bit plane containing error needs more number of bits, but the gain in the quality is marginal. These cases are considered, by employing the skip mode [10]. However, in ERC under-estimation case, the errors in these decoded bit plane cause artifacts in the reconstructed frame, although the difference between the SI and the original bins is less. To avoid this, motion adaptive reconstruction is employed in each band, based on the accuracy of the estimated SI calculated using the motion vectors and the residue. Let us consider the bidirectional motion vectors of the 4x4 block B as (𝑀 𝑉𝑥𝑖 , 𝑀 𝑉𝑦𝑖 ), 𝑖 = 1, 2 and the motion compensated residue between the two reference frames be 𝑅𝑗 . ∑ ∑ 1 If 𝑖=1,2 (∣𝑀 𝑉𝑥𝑖 ∣ + ∣𝑀 𝑉𝑦𝑖 ∣) < 𝜃2 𝑎𝑛𝑑 16 𝑗∈𝐵 𝑅𝑗 < 𝜃3

Integration of iterative refinement tools at the decoder At the decoder, initial SI and the correlation noise are estimated as in the DVC codecs [3] for the given WZ frame. ”A priori” soft input information for the bit planes encoded in the WZ mode in the decoding band is calculated with respect to the estimated SI coefficient and the previous decoded bit planes. Following this, the errors in the bit planes of the each band are to be corrected from the parity bits sent from the encoder. for which, we have employed iterative decoding tools. 1) Bit plane level iterative decoding: Decoding of the bit planes is carried out, starting from the MSB to the LSB, repeated iteratively for 𝑁1 number of iterations. In the decoding stage of every bit plane, the input LLR is calculated using the information of the remaining bit planes and the LDPCA decoder is run up to 𝑁2 number of times using the same parity bits of the bit plane. The change in LLR with successive runs of LDPCA is made finer and finer, by utilizing the information of the remaining bit planes and by the iterative refinement. The LLR of the bit plane is calculated by combining the symbol probabilities of the current decoding bit plane, calculated from the correlation noise, SI transform coefficient and the probabilities of the remaining decoded bit planes as in [6]. 𝑁𝑡 =

∑

𝑃 𝑟(𝑋∣𝑌, 𝑏𝑖 = 0)

𝑋∈𝑆

𝐷𝑡 =

∑ 𝑋∈𝑆

∏

𝑃 𝑟(𝑡) (𝑏𝑘 )

𝑘=1,.𝑖−1

𝑃 𝑟(𝑋∣𝑌, 𝑏𝑖 = 1)

∏

∏ 𝑘=𝑖+1,.𝑀

𝑃 𝑟(𝑡) (𝑏𝑘 )

𝑘=1,.𝑖−1

𝐿𝑡 (𝑏𝑖 ) = 𝑙𝑜𝑔

∏ 𝑘=𝑖+1,.𝑀

𝑁𝑡 𝐷𝑡

𝑃 𝑟(𝑡−1) (𝑏 𝑘 ) (3) 𝑃 𝑟(𝑡−1) (𝑏𝑘 ) (4) (5)

where, M is the bit number of bits used to represent the symbol X and S is the set of the symbols 𝑆 ∈ (1, 2...2𝑀 −1). 𝑃 𝑟(𝑡) (𝑏𝑘 ) is the probability of the 𝑘 𝑡ℎ bit in the 𝑡𝑡ℎ iteration. 𝑃 𝑟(𝑋∣𝑌, 𝑏𝑖 = 0) is the symbol probability of the X conditioned on the SI coefficient Y, initial estimated correlation coefficient of the band and bit 𝑏𝑖 . In the LLR calculation of the 𝑖𝑡ℎ bit plane in 𝑡𝑡ℎ iteration, probabilities calculated in the 𝑡𝑡ℎ iteration are used for the bit planes 1, 2..𝑖 − 1, whereas for the bit planes from 𝑖, 𝑖 + 1...𝑚 it is from the (𝑡 − 1)𝑡ℎ iteration, as shown in eq. 5. Using the same parity bits and the calculated LLR for the bit plane, a belief propagation based LDPCA decoding is done up to 𝑁2 iterations. Following this, the probabilities of the decoding bit plane are calculated using the output LLR of the LDPCA decoder, resulting in the 𝑖𝑡ℎ bit plane probabilities in the 𝑡𝑡ℎ iteration.

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MMSE reconstruction step is used only with the correctly decoded bit planes of the each decoded DCT coefficient. If there are no correctly decoded bit planes then the final coefficient is replaced with the initial SI. Else Soft reconstruction is employed using the LLR output of all the decoded bit planes for getting the final reconstruction coefficients. End Soft reconstruction is employed for the blocks having the unidirectional motion vectors. Next, IDCT is performed to get the final reconstructed block. After obtaining the final decoded frame, the same decoding process, as explained earlier is carried out for the remaining WZ frames in the GOP. 5) SI refinement at final stage: After decoding all the WZ frames in the present GOP, WZ frame in the 𝐿0 will be having the decoded reference frames 𝐿1 , which are temporally closer neighbors compared to the one used in the decoding stage. At this stage, SI for 𝐿0 can be refined by using the 𝐿1 frames. With the use of refined SI, it is expected that the remaining errors, if any, in the bit planes can be corrected. First of all, for every 8x8 block, the MAD of the motion compensated residue between the two key frames is calculated as follows. 𝐷=

Figure 3.

RD Performance comparison of the Foreman Sequence

rate of 15 frames per second with GOP-4. In our simulation, 𝜃1 =6, 𝜃2 =8, 𝜃3 =5, w=0.5 and 𝜃4 =7 are used and selected empirically. We used four SI iterations. In the bit plane iterative decoding loop, we set 𝑁1 =5 and 𝑁2 =100. The RD performance of the proposed scheme is compared with the DISCOVER, ERC [16] and the H.264 intra coding scheme and the plot is shown in Fig. 3 and Fig. 4 for Foreman and Coastguard sequence. From the RD plot, our proposed scheme is 1dB below the DISCOVER codec and 2.5dB below the H.264 intra codec for the Foreman sequence. This is expected as DISCOVER is a feedback channel based DVC scheme. To compare with the no feedback ERC scheme [16], the tools employed in [16] are applied for GOP-4. Our scheme offers a gain of 2 dB. The gain is mainly due to the adaptive selection of the model and the blocks added at the decoder over to the scheme in [16].

8 8 1 ∑∑ ∣(𝐾1 (𝑖+𝑚𝑥1 , 𝑗+𝑚𝑦1 )−𝐾2 (𝑖+𝑚𝑥2 , 𝑗+𝑚𝑦2 ))∣ 64 𝑖=1 𝑗=1 (6)

where, (𝑚𝑥𝑖 , 𝑚𝑦𝑖 ), 𝑖 = 1, 2 are the set of motion vectors for the key frames 𝐾1 and 𝐾2 generated in the final refined SI stage. If D is greater than threshold (𝜃4 ), then the block SI is refined using the frames in 𝐿1 by the motion refinement. The difference between the two compensated blocks is used for the CN estimation. With the new SI and CN, erroneous bands are re-decoded with the refined SI and CN using the above bit plane decoding procedure. If there is any improvement in the number of decoded bit planes with refined SI, then the refined SI is used as the final SI for reconstruction. The 𝐿1 frames can be refined using the updated frame in 𝐿0 , as the quality is better compared to the one used in the previous decoding stages of frames in 𝐿1 . This provides a way to use the iterative refinement until no more errors in the bit planes are correctable in the 𝐿0 and 𝐿1 frames. If there is no improvement in the number of decoded bit planes with refined SI, the previous SI is used as the final SI for the reconstruction.

In Coastguard sequence, the proposed scheme provides a gain of about 1 dB at low bit rate compared to the H.264 intra and is 0.8 dB below the DISCOVER codec. The gap is due to the over estimation of number of bits for the frame in the 𝐿0 and for certain frames, the rate estimation is not quite accurate, leading to the degradation of the quality for the certain frames in the GOP. If the frame is decoded without any errors, then the quality of the reconstructed frame is high compared to the one decoded with DISCOVER, due to the use of refinement scheme at the decoder, which can partially compensate the over-estimation of the bits. The proposed scheme gives better RD performance compared to the recent schemes, but with the increase of the encoder complexity. The average increase of the encoder complexity in the SI generation process is 5 times compared to the SI generated by using the average interpolation. By considering the dependency between the WZ frames in higher GOP’s, there is a need to develop the better ERC schemes and a study of the complexity scalable ERC is needed in the DVC framework.

III. EXPERIMENTAL RESULTS In this section, we present the experimental results to demonstrate the performance of the proposed scheme. The RD performance of the proposed scheme is tested with two standard QCIF sequences Foreman and Coastguard at the

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[8] J. Ascenso, C. Brites, F. Dufaux, A. Fernando, T. Ebrahimi, F. Pareira, and S. Tubaro, “The VISNET II DVC Codec: Architecture, Tools and Performance,” Proc. of the 18th European Signal Processing Conference (EUSIPCO 2010), 2010. [9] R. Martins,C. Brites, J. Ascenso, and F. Pereira, “Refining Side Information for Improved Transform Domain Wyner-Ziv Video Coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 9, Sep. 2009. [10] J. Slowack, S. Mys, J. Skorupa, N. Deligiannis, P. Lambert, A. Munteanu and R. Walle,“Rate-distortion driven decoder-side bit plane mode decision for distributed video coding,” Signal Processing Image Communication, Vol.25, no.9, pp.660-673, 2010. Figure 4.

RD performance comparison of the Coastguard sequence

[11] S. Mys, J. Slowack, J. Skorupa, N. Deligiannis, P. Lambert, A. Munteanu and R. Walle,“Decoder-driven mode decision in a block-based distributed video codec,” Multimedia tools and applications, 2011.DOI 10.1007/s11042-010-0718-5.

IV. CONCLUSION In this paper, we proposed an improved ERC scheme with GOP-4. The proposed scheme includes the encoder adaptive rate estimation considering the dependency across WZ frames. At the decoder, we added the iterative refinement and iterative decoding techniques to correct the bit planes errors by including additional tools like motion adaptive reconstruction and SI refinement after decoding all the frames in the GOP.

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