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Predictive side decoding for humancentered multiple description image coding
EURASIP Journal on Wireless Communications and Networking volume 2020, Article number: 93 (2020)
Abstract
Multiple description coding (MDC) provides a favorable solution for humancentered image communication, which takes into account people’s varying watching situations as well as people’s demand for realtime image display. As an effective technique for MDC, threedescription lattice vector quantization (3DLVQ) is considered for image coding in this paper. Based on intra and intercorrelation in the 3DLVQ index assignment as well as wavelet intrasubband correlation, a novel predictive decoding method for 3DLVQbased image coding is proposed to enhance side decoding performance, which attempts to predict lost descriptions (sublattice points) in a good way for better reconstructions of wavelet vectors (fine lattice points) in the side decoding. Experimental results validate effectiveness of the proposed decoding scheme in terms of ratedistortion performance.
Introduction
The revolutionary computing technology advances have changed almost every aspect of human lives [1]. However, these changes intended to be positive are not always so. Since a lot of computing technologies are designed ignorant of human’s needs or social cultural contexts, these technologies are complex, difficult to use, and demanding, especially for ordinary people who do not possess skilled knowledge [1]. These issues bring a need to develop new computing paradigms that focus more on people instead of machines. Humancentered computing (HCC) [2–9] aims to bridge gaps between multiple disciplines and tries to design and implement the computing systems that support human endeavor.
According to [1], HCC system and algorithm design needs to take into account individual human abilities and limitation, social and cultural awareness, and the adaptability across individuals and specific situations, for example, designing recommender systems or recommending services that consider individual’s social and cultural context [10–17]. An interesting topic in multimedia applications in HCC is the adaptation of multimedia communication to varying demands of different people, whose communication channels may have varying bandwidth and loss probabilities. When packet loss occurs during online image browsing, people tend to prefer viewing a degraded version of a whole image immediately instead of waiting and staring at a partially displayed fine image. The design of humancentered image coding scheme that takes into account people’s varying watching situations as well as people’s demand for realtime image display is a challenging problem.
Multiple description coding (MD coding or MDC) [18] provides a favorable solution to this problem. Although the reliability of multimedia communication can be improved from the perspective of multicore realtime system design [19–21] or load balancing of cloudedge computing [22–27], MDC offers an errorresilient source coding method to combat information loss over lossy networks without retransmission. MDC generates different encoded versions for the same source. Each version is referred to as a description and transmitted separately over unreliable networks. Each description can provide a degraded version of the source independently, while a finer reconstruction quality can be obtained with increasing number of descriptions received. Generally, the decoding of one or partial descriptions is known as side decoding corresponding to side distortions, while the decoding of all the descriptions is central decoding resulting in a central distortion [28]. Using MDC, people with varying bandwidth can select different number of descriptions that correspond to different reconstruction qualities. During network congestion, people can get access to a coarsely reconstructed source immediately, instead of waiting for retransmission of all the lost packets.
Vaishampayan introduced the earliest practical MD technique known as multiple description scalar quantizer (MDSQ) [29]. MDSQ generates descriptions by performing scalar quantization, followed by an index assignment. A wavelet image coding based on MDSQ was developed in [30]. Another waveletbased MD image coding scheme is proposed in [31] for image transmission with mixed impulse noise, where multiobjective evolutionary algorithm is used to solve the side quantization optimization problem and the parameter optimization problem of the denoising filter simultaneously.
Multiple description lattice vector quantization (multiple description LVQ or MDLVQ) was later developed in [32], and a study on optimal MDLVQ design was presented in [33]. MDLVQ generates descriptions by performing vector quantization first, and then, an index assignment maps a fine lattice point to multiple sublattice points. An image coding scheme based on twodescription LVQ was developed in [34], which shows better coding performance than the corresponding MDSQbased counterpart [30]. In [35], the design of Mdescription LVQ is investigated, where the MDLVQ index assignment design is translated into a transportation problem. The effectiveness of the proposed index assignment design in [35] is verified under highresolution assumption. In [36], an analytical expression for optimal entropyconstrained asymmetric MDLVQ design is presented, which allows unequal packetloss probabilities and side entropies. In [37], the design of symmetric MD coinciding LVQ is proposed, where the coinciding sublattices refer to sublattices with the same index but generated by different generator matrices. The developed MD coinciding LVQ scheme is applied to standard test images.
Other MD schemes include using forward error correction codes [38], MDC via polyphase transform and selective quantization [39], set partitioning of hierarchical trees (SPHIT)based image MDC [40], and a JPEG 2000based MD approach presented in [41]. In [42], a just noticeable difference (JND)based MD image coding scheme is proposed utiltizing the charactersitics of human visual model. In [43], an adaptive reconstructionbased MD image coding scheme is proposed with randomly offset quantizations. Deep learning approaches [44] have been applied in the MDC. In [45], a standardcompliant multiple description coding framework is proposed, where the input image is polyphase downsampled to form two descriptions for the standard codec, while during decoding deep convolutional neural networks are utilized to conduct artifact removal and image superresolution to enhance reconstructed image quality. In [46], MDC and convolutional autoencoders are combined for image compression to achieve high coding efficiency.
Besides traditional images, a few research works on MDC target at 3D depth images or singleview and multiview video sequences. In [47], observing that the 3D depth images have special characteristics, which can be classified into edge blocks and smooth blocks, a twodescription LVQ scheme is proposed for efficient compression of 3D depth images. In [48], a novel coding scheme has been proposed for video sequences based on the spatialtemporal masking characteristics of human visual system. In [49], the multiview sequence is spatial polyphase subsampled and “crossinterleaved” sampling grouped to generate two subsequences, and an MDC scheme is proposed which directly reuses the computed modes and prediction vectors of one subsequence to the other one. This work is extended in [50], where one subsequence is directly coded by joint multiview video coding (JMVC) encoder, and the other subsequence selectively chooses the prediction mode and the prediction vector of the coded subsequence to improve the ratedistortion performance. On the decoder side, the side reconstruction quality is improved using a gradientbased interpolation.
Among the abovementioned works, most of them center on twochannel MDC or twodescription coding. Comparing with twodescription MDC, moredescription case is able to provide better robustness against description loss, especially for networks with high loss ratios. However, redundancy increases apparently with the increasing number of descriptions. Threedescription coding may thereby be a good tradeoff choice in some cases. On the other hand, compared with MDSQ, MDLVQ exhibits better coding efficiency and the ease of extension to moredescription coding. Therefore, a threedescription lattice vector quantization (3DLVQ)based image coding scheme is considered in this paper.
The general design of 3DLVQ is concerned with index assignment, which is discussed in [33] and [51]. Here, we consider how to take good advantage of the index assignment result for better reconstruction quality in image decoding. For the vector reconstruction at the decoder side in the case of some descriptions (i.e., sublattice points in MDLVQ) being lost, the existing MDLVQ coding schemes employ a simple side decoding of each vector individually based on the sublattice points of the vector. We observe a good correlation characteristic of the 3DLVQ index assignment result, which can be exploited to enhance side decoding for memory source. Specifically in the context of wavelet image coding, a predictive side decoding method is proposed accordingly to improve reconstruction quality in side decoding. Compared with the existing work in [33, 51] which only decodes the received sublattice points during description losses, the proposed scheme can predict the lost sublattice points based on index correlation.
The main contributions of this paper can be summarized as follows:
∙ The intra and intercorrelation between sublattice points in the 3DLVQ index assignment has been analyzed and discussed, followed by the correlation discussion of wavelet intrasubbands.
∙ Based on correlation discussion, a novel predictive decoding method for 3DLVQbased image coding is proposed to enhance side decoding performance. The performance of the proposed predictive decoding scheme is verified by experimental results.
The remainder of the paper is structured as follows. Section 2 provides a 3DLVQbased image coding scheme. Section 3 presents a novel predictive side decoding approach. Experimental settings and results are presented in Sections 4 and 5, respectively, while Section 6 concludes the paper.
Threedescription LVQbased image coding
In this section, we first provide a concise description of 3DLVQ and then present a 3DLVQbased image coding scheme.
3DLVQ
For a given lattice Λ in the Ldimensional Euclidean space, a sublattice Λ^{′}⊆Λ is said to be geometrically similar to Λ, if Λ^{′} can be obtained from Λ by applying a scaling, rotation, or reflection. The index number N of the sublattice Λ^{′} is defined as the number of elements of Λ (fine lattice points) in each Voronoi cell of Λ^{′}. 3DLVQ aims to map one fine lattice point λ (λ∈Λ) to three sublattice points λ1′, λ2′, and λ3′(λ1′,λ2′,λ3′∈Λ^{′}) based on a bijective labeling function α(.) (also known as index assignment) as:
for minimizing the side distortions when only one or two sublattice points are received. The overall 1description side distortion D_{s1,λ} and the overall 2description side distortion D_{s2,λ} are given as:
respectively, where the midpoint of two received sublattice points is taken as the reconstructed vector for the 2descriptionbased side decoding. The optimal index assignment design to minimize the side distortions or the expected distortion is a challenging task, and the index assignment based on A_{2} lattice can be found in [33] and [51]. Figure 1 shows an example of the labeling function obtained with the index assignment result in [33] and [51] based on A_{2} lattice with index number N=31, which has been shown to minimize the side distortions. For instance, the lattice point “OAB” in Fig. 1 is represented by the three sublattice points “O,” “A,” and “B,” while another lattice point “BOO” is mapped to the three sublattice points “B,” “O,” and “O.” In this paper, we consider the 3DLVQ with the optimal index assignment as shown in Fig. 1.
3DLVQbased image coding
As in [34], a simple 3DLVQbased image encoding scheme is shown in Fig. 2. As a popular technique for image compression, discrete wavelet transform (DWT) can provide multiresolution representation and subband decomposition for images and capture feature information in horizontal, vertical, and diagonal directions [52]. DWT is considered for image coding in this paper. After applying a DWT to the input image, an input vector x is constructed in a subband. It is then quantized to a (fine) lattice point λ(x), which is mapped to three sublattice points λ1′(x), λ2′(x), and λ3′(x) to be transmitted in separate channels after performing arithmetic coding.
At the receiver, decoding is the exact reverse of encoding. Due to network congestion or channel errors, some channels of information (descriptions) may be lost. Therefore, three different types of 3DLVQ decoders may be needed, that is, onedescriptionbased and twodescriptionbased side decoding as well as threedescriptionbased central decoding. Denote by \(\hat {\textbf {x}}\) the reconstructed vector x. If all the three sublattice points of vector x are received, the central decoder yields α^{−1}(λ1′(x),λ2′(x),λ3′(x))=λ(x), where α^{−1} is the inverse function of the labeling function α. If two sublattice points are received while one is lost, the conventional twodescriptionbased side decoder simply takes the average of the two sublattice points λi′(x) and λj′(x) (1≤i,j≤3,i≠j) as the reconstructed vector:
In the case of only one sublattice point λi′(x) being received, the conventional onedescriptionbased side decoder just uses the received sublattice point for the reconstruction:
In the following section, We will propose a more effective vector reconstruction method to improve the side decoding performance by taking advantage of the correlation of sublattice points in the 3DLVQ index assignment and the wavelet intrasubband correlation characteristics.
3DLVQbased predictive side decoding
Correlation discussion
As can be seen from Fig. 1, each fine lattice point is mapped to an ordered 3tuple with the three sublattice points being as close as possible to the fine lattice point for minimizing side distortions [33, 51]. In this way, we can see that there is a strong intracorrelation among the three sublattice points for a fine lattice point. More importantly, there exists a substantial intercorrelation among neighboring fine lattice points in terms of their corresponding sublattice points. In other words, neighboring fine lattice points share most sublattice points in the index assignment. In Fig. 1, for instance, the fine lattice point labeled as “OOA” shares at least two sublattice points with its six closest neighbors “AOO,” “OAO,” “BOO,” “OAB,” “AOB,” and “OAF,” regardless of the order. Statistically, we observe from the figure that the immediately neighboring fine lattice points have the same three sublattice points (but in different order) with a probability of 78/186, while they share two sublattice points with a probability of 108/186. That is to say, these immediately neighboring fine lattice points share at least two sublattice points. As the distance between two fine lattice points increases, they have fewer sublattice points in common.
On the other hand, it is well known that a wavelet image normally exhibits strong intrasubband correlation especially in lowfrequency subbands, as the discrete wavelet transform redistributes the energy of the image into different subbands. Onedimensional DWT passes the signal through a lowpass filter and a highpass filter simultaneously, providing approximation coefficients (lowfrequency subband) and detail coefficients (highfrequency subband), respectively. For twodimensional DWT performed on images, one level of transform generates four subbands. The subband with lowpass filters in both horizontal and vertical directions is termed as the “LL” subband. Similarly, the subbands resulting from a highpass filter in the horizontal direction and a lowpass filter in the vertical direction, a lowpass filter in the horizontal direction and a highpass filter in the vertical direction, and highpass filters in both directions are termed as the “HL,” “LH,” and “HH” subbands, respectively. As an example, twostage wavelet decomposition of the image “Couple” is shown in Fig. 3. It can be seen that coefficients in subband “LL” exhibit high correlation in both horizontal and vertical directions due to the fact that “LL” is the lowpass filtered version of the original image in both directions. Likewise, the coefficients in the “HL” and “LH” subbands are highly correlated either vertically or horizontally. However, the coefficients in subband “HH” have less correlation in the subband of high frequency in both directions.
In view of the concurrent correlations in the 3DLVQ index assignment and wavelet subbands, with properly constructed vectors based on the correlation of wavelet coefficients, the neighboring wavelet vectors will most likely share some sublattice points, which motivates us to develop a better side decoding approach by predicting lost descriptions (sublattice points) using neighboring information. To exploit the directional correlations in the wavelet subbands, we consider constructing a vector for the “LH” subband with two horizontally neighboring coefficient, whereas for the “HL” subband, a vector is constructed with two vertical neighboring coefficients. For simplicity, vectors for the “LL” and “HH” subbands are also constructed horizontally.
Proposed 3DLVQ side decoding with prediction
Consider a wavelet vector x which is mapped to (λ1′(x),λ2′(x),λ3′(x)) in the 3DLVQ coding, where λk′(x) is assigned to kth description. We will first study the twodescriptionbased side decoding, that is, the reconstruction of the vector x if one description such as description k is lost (λk′(x) is missing). As discussed above, there is strong intra and intercorrelation in the assignment of sublattice points for the 3DLVQ mapping, while neighboring wavelet vectors may most likely share most or all sublattice points. Therefore, it is reasonable to predict the lost λk′(x) from those received sublattice points for the vector x as well as from its neighboring vectors. A list of sublattice point candidates can be formed for the estimation of λk′(x). Subsequently, we can reconstruct the vector x by taking each sublattice point in the list as an estimate of the missing sublattice point for decoding and finally averaging the decoded results.
As an example, we consider the vector x and its neighboring vector y labeled as (λ1′(y),λ2′(y),λ3′(y)) with description 1 being lost. Then, we receive {λ2′(x),λ3′(x)} for vector x and {λ2′(y),λ3′(y)} for vector y at the decoder side, while λ1′(x) and λ1′(y) in description 1 are missing. Based on the above discussion, the candidate list for estimating the lost λ1′(x) can be obtained as {λ2′(x),λ3′(x),λ2′(y),λ3′(y)}, in which each element may be a good prediction. Note that these sublattice points in the list may be duplicate. We can thereby use all the candidates in the list one by one as an estimate of the missing sublattice point for decoding and then take the average as the reconstruction \(\hat {\textbf {x}}\). That can be represented as:
If there are more neighboring vectors of x, their sublattice points can be included in the candidate list. Note that there may be some invalid 3tuple combinations with the prediction scheme, which are not decodable by the inverse mapping function. In that case, those sublattice points causing invalid combinations are removed from the candidate list. Then, all the valid combinations based on the final candidate list are decoded and averaged as the final reconstruction of x.
We now consider onedescriptionbased side decoding where only one description is received while the other two are missing. Assuming description 1 and description 2 are lost, only the sublattice points {λ3′(x)} and {λ3′(y)} are received for the vector x and its neighboring vector y, respectively. Similarly, we can also construct a candidate list of {λ3′(x),λ3′(y)}. Instead of estimating the two missing sublattice points which are harder or unreliable to be predicted based on one received sublattice point and its neighbor, we simply use the sublattice points in the list as possible reconstructions for vector x followed by an averaging that is \(\hat {\textbf {x}} = (\lambda _{3}'(\textbf {x}) + \lambda _{3}'(\textbf {y}))/2\). Like the twodescriptionbased side decoding, we also need to perform a validation for each candidate in the list by checking whether the candidate point is the same as or immediately neighboring to the received sublattice point {λ3′(x)}. Invalid sublattice points are removed from the list. Then, all the valid sublattice points are averaged to obtain the final reconstruction \(\hat {\textbf {x}}\).
In the above, we show the way to obtain the reconstruction given one neighboring vector for vector x, which can be extended to the case of more neighboring vectors. Consider a twodimensional wavelet image, there are four directly neighboring vectors for a vector. Denote by λ(i,j) the current vector to be decoded, while λ(i−1,j),λ(i+1,j) and λ(i,j−1),λ(i,j+1) are the four adjacent vectors horizontally and vertically, respectively.
For the band “LL,” in view of both horizontal and vertical correlation, prediction for the current vector λ(i,j) can utilize the four adjacent vectors. All the received sublattice points of vector λ(i,j) and these four neighboring vectors are put into the candidate list with possible duplicates. For the band “HL” exhibiting the vertical correlation, the two vertically adjacent vectors λ(i,j−1) and λ(i,j+1) are employed for the prediction. Therefore, the candidate list consists of received sublattice points for λ(i,j),λ(i,j−1), and λ(i,j+1). For band “LH” showing the horizontal correlation, we use horizontally adjacent vectors λ(i−1,j) and λ(i+1,j) in the prediction. Consequently, the candidate list comprises the received sublattice points for λ(i,j),λ(i−1,j), and λ(i+1,j). For the band “HH,” no prediction is considered and the conventional MDLVQ decoding is performed, that is, the received sublattice point or the average of two received sublattice points is used as the reconstruction of the current vector for onedescriptionbased or twodescriptionbased side decoding. Figure 4 illustrates the predictive side decoding using neighboring vectors with respect to the different subbands.
Experimental methods
Five standard 512×512 images, “Lena,” “Couple,” “Baboon,” “Aerial,” and “Goldhill,” were tested in the experiment. The input image was applied with a discrete wavelet transform (DWT), where fourstage decomposition with the 10/18 Daubechies wavelet was employed. As mentioned before, to exploit the directional correlations in the wavelet subbands, we constructed a 2×1 vector with two horizontally neighboring coefficients in the “LH” subband or two vertically neighboring coefficients in the “HL” subband, while the vectors in the “LL” and “HH” subbands could be formed horizontally or vertically (horizontally in our experiments). Such a vector x is then quantized to a (fine) lattice point λ(x), which was mapped to three sublattice points λ1′(x), λ2′(x), and λ3′(x) based on the predesigned index assignment. Lastly, adaptive threeorder arithmetic coding was applied to compress the three sequences of sublattice indexes. The three produced descriptions may be transmitted in separate channels. At the receiver, the conventional decoding method and the proposed predictive decoding method were used to reconstruct images based on the received descriptions. Note that our focus is to test the effectiveness of the proposed side decoding in terms of ratedistortion performance, as compared to the conventional side decoding [51] as shown in (4) and (5). We implemented both the algorithms with the sublattice index number N=31.
Experimental results and discussion
Ratedistortion curves are plotted in Fig. 5 to compare the two decoding schemes in decoding all the five testing images. It can be seen that our proposed predictive scheme consistently outperforms the conventional method in both onedescriptionbased and twodescriptionbased side decoding, where up to 1.68 dB (at 0.531 bpp for “Goldhill”) and 1.64 dB (at 0.531 bpp for “Goldhill”) gains are obtained in the cases of 2description side decoding and 1description side decoding, respectively. Reconstructed images for “Lena” in the case of losses of one and two descriptions are shown in Fig. 6 for a subjective visual comparison. In the figure, the proposed scheme can achieve 1.37 dB gain at 0.537 bpp in the 2description side decoding and 1.25 dB gain at 1.012 bpp in the 1description side decoding over the conventional method for “Lena,” respectively. The coding gain tends to become more significant at lower bit rates where the side distortion is normally larger, as expected. With a higher coding bit rate, the conventional side decoding may also reconstruct a vector fairly well even with one or two received sublattice points due to a finer quantization in that case, leaving less room of improvement for the predictive side decoding.
Conclusions
In this paper, we consider the design of humancentered image coding scheme that can adapt to people’s varying watching situations and consider people’s demand for realtime image display. Specifically, a novel predictive side decoding scheme for 3DLVQbased image coding has been proposed. In view of the strong intra and intercorrelation in the index assignment of 3DLVQ mapping as well as the intrasubband correlation exhibited in the lowfrequency wavelet subbands, we have developed an effective prediction approach for lost descriptions (sublattice points) to enhance side decoding performance. The prediction scheme adapts to the different subbands with varying intrasubband correlation characteristics. Experimental results have substantiated the effectiveness of the proposed predictive side coding in reducing side distortions significantly for both twodescriptionbased and onedescriptionbased cases. As compared to the conventional side decoding method, the proposed decoding scheme has shown up to 1.68 dB and 1.64 dB performance gains in the cases of 2description side decoding and 1description side decoding, respectively, in our experiments.
Availability of data and materials
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
Abbreviations
 3DLVQ:

Threedescription lattice vector quantization
 DWT:

Discrete wavelet transform
 HCC:

Humancentered computing
 JMVC:

Joint multiview video coding
 JPEG:

Joint photographic expert group
 LVQ:

Lattice vector quantization
 MD:

Multiple description
 MDC:

Multiple description coding
 MDSQ:

Multiple description scalar quantization
 MDLVQ:

Multiple description lattice vector quantization
 SPIHT:

Set partitioning of hierarchical trees
References
 1
A. Jaimes, D. GaticaPerez, N. Sebe, T. S. Huang, Guest editors’ introduction: humancentered computing–toward a human revolution. Computer. 40(5), 30–34 (2007).
 2
M. L. Dertouzos, T. Foreword ByBernersLee, The Unfinished Revolution: Humancentered Computers and What They Can do for Us (HarperInformation, 2002).
 3
A. Jaimes, N. Sebe, D. GaticaPerez, in Proceedings of the 14th ACM International Conference on Multimedia. Humancentered computing: a multimedia perspective, (2006), pp. 855–864. https://doi.org/10.1145/1180639.1180829.
 4
N. Sebe, in Handbook of Ambient Intelligence and Smart Environments. Humancentered computing (Springer, 2010), pp. 349–370.
 5
L. Bunch, J. M. Bradshaw, R. R. Hoffman, M. Johnson, Principles for humancentered interaction design, part 2: can humans and machines think together?IEEE Intell. Syst.30(3), 68–75 (2015).
 6
P. Garcia Lopez, A. Montresor, D. Epema, A. Datta, T. Higashino, A. Iamnitchi, M. Barcellos, P. Felber, E. Riviere, Edgecentric computing: vision and challenges. ACM SIGCOMM Comput. Commun. Rev.45(5), 37–42 (2015).
 7
S. Choi, Understanding people with human activities and social interactions for humancentered computing. Hum. Centric Comput. Inf. Sci.6(1), 9 (2016).
 8
X. Ren, Rethinking the relationship between humans and computers. IEEE Comput.49(8), 104–108 (2016).
 9
M. Chen, F. Herrera, K. Hwang, Cognitive computing: architecture, technologies and intelligent applications. IEEE Access. 6:, 19774–19783 (2018).
 10
L. Qi, X. Zhang, W. Dou, Q. Ni, A distributed localitysensitive hashingbased approach for cloud service recommendation from multisource data. IEEE J. Sel. Areas Commun.35(11), 2616–2624 (2017).
 11
W. Gong, L. Qi, Y. Xu, Privacyaware multidimensional mobile service quality prediction and recommendation in distributed fog environment. Wirel. Commun. Mob. Comput.2018: (2018). https://doi.org/10.1155/2018/3075849.
 12
S. Kumar, M. Singh, Big data analytics for healthcare industry: impact, applications, and tools. Big Data Min. Anal.2(1), 48–57 (2018).
 13
Y. Liu, S. Wang, M. S. Khan, J. He, A novel deep hybrid recommender system based on autoencoder with neural collaborative filtering. Big Data Min. Anal.1(3), 211–221 (2018).
 14
L. Qi, X. Zhang, W. Dou, C. Hu, C. Yang, J. Chen, A twostage localitysensitive hashing based approach for privacypreserving mobile service recommendation in crossplatform edge environment. Futur. Gener. Comput. Syst.88:, 636–643 (2018).
 15
A. Ramlatchan, M. Yang, Q. Liu, M. Li, J. Wang, Y. Li, A survey of matrix completion methods for recommendation systems. Big Data Min. Anal.1(4), 308–323 (2018).
 16
C. Zhang, M. Yang, J. Lv, W. Yang, An improved hybrid collaborative filtering algorithm based on tags and time factor. Big Data Min. Anal.1(2), 128–136 (2018).
 17
H. Liu, H. Kou, C. Yan, L. Qi, Link prediction in paper citation network to construct paper correlation graph. EURASIP J. Wirel. Commun. Netw.2019(1), 1–12 (2019).
 18
V. K. Goyal, Multiple description coding: compression meets the network. IEEE Signal Process. Mag.18(5), 74–93 (2001).
 19
J. Zhou, J. Sun, X. Zhou, T. Wei, M. Chen, S. Hu, X. S. Hu, Resource management for improving softerror and lifetime reliability of realtime MPSoCs. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. (2018). https://doi.org/10.1109/tcad.2018.2883993.
 20
J. Zhou, J. Sun, P. Cong, Z. Liu, X. Zhou, T. Wei, S. Hu, Securitycritical energyaware task scheduling for heterogeneous realtime MPSoCs in IoT. IEEE Trans. Serv. Comput. (2019). https://doi.org/10.1109/tsc.2019.2963301.
 21
J. Zhou, X. S. Hu, Y. Ma, J. Sun, T. Wei, S. Hu, Improving availability of multicore realtime systems suffering both permanent and transient faults. IEEE Trans. Comput.68(12), 1785–1801 (2019).
 22
X. Xu, Q. Cai, G. Zhang, J. Zhang, W. Tian, X. Zhang, A. X. Liu, An incentive mechanism for crowdsourcing markets with social welfare maximization in cloudedge computing. Concurr. Comput. Pract. Experience, 4961 (2018). https://doi.org/10.1002/cpe.4961.
 23
X. Xu, R. Mo, F. Dai, W. Lin, S. Wan, W. Dou, Dynamic resource provisioning with fault tolerance for dataintensive meteorological workflows in cloud. IEEE Trans. Ind. Inform. (2019). https://doi.org/10.1109/tii.2019.2959258.
 24
X. Xu, X. Liu, Z. Xu, C. Wang, S. Wan, X. Yang, Joint optimization of resource utilization and load balance with privacy preservation for edge services in 5G networks. Mob. Netw. Appl., 1–12 (2019). https://doi.org/10.1007/s11036019014488.
 25
X. Xu, Y. Li, T. Huang, Y. Xue, K. Peng, L. Qi, W. Dou, An energyaware computation offloading method for smart edge computing in wireless metropolitan area networks. J. Netw. Comput. Appl.133:, 75–85 (2019).
 26
X. Xu, Q. Liu, Y. Luo, K. Peng, X. Zhang, S. Meng, L. Qi, A computation offloading method over big data for IoTenabled cloudedge computing. Futur. Gener. Comput. Syst.95:, 522–533 (2019).
 27
X. Xu, S. Fu, L. Qi, X. Zhang, Q. Liu, Q. He, S. Li, An IoToriented data placement method with privacy preservation in cloud environment. J. Netw. Comput. Appl.124:, 148–157 (2018).
 28
Y. Xu, C. Zhu, in 2009 Fifth International Conference on Image and Graphics. Joint multiple description coding and network coding for wireless image multicast, (2009), pp. 819–823. https://doi.org/10.1109/icig.2009.73.
 29
V. A. Vaishampayan, Design of multiple description scalar quantizer. IEEE Trans. Inf. Theory. 39(3), 821–834 (1993).
 30
S. D. Servetto, K. Ramchandran, V. A. Vaishampayan, K. Nahrstedt, Multiple description wavelet based image coding. IEEE Trans. Image Process.9(5), 813–826 (2000).
 31
H. Kusetogullari, A. Yavariabdi, Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images. Appl. Soft Comput.73:, 1039–1052 (2018).
 32
V. A. Vaishampayan, N. J. A. Sloane, S. D. Servetto, Multiple description vector quantization with lattice codebooks: design and analysis. IEEE Trans. Inf. Theory. 47(5), 1718–1734 (2001).
 33
X. Huang, Multiple Description Lattice Vector Quantization. Master’s Thesis (McMaster University, Department of Electrical & Computer Engineering, Canada, 2006).
 34
H. Bai, C. Zhu, Y. Zhao, Optimized multiple description lattice vector quantization for wavelet image coding. IEEE Trans. Circ. Syst. Video Technol.17(7), 912–917 (2007).
 35
M. Liu, C. Zhu, Mdescription lattice vector quantization: index assignment and analysis. IEEE Trans. Signal Process.57(6), 2258–2274 (2009).
 36
J. Ostergaard, R. Heusdens, J. Jensen, nchannel asymmetric entropyconstrained multipledescription lattice vector quantization. IEEE Trans. Inf. Theory. 56(12), 6354–6375 (2010).
 37
E. Akhtarkavan, M. F. M. Salleh, Multiple descriptions coinciding lattice vector quantizer for wavelet image coding. IEEE Trans. Image Process.21(2), 653–661 (2011).
 38
R. Puri, K. Ramchandran, in Proc. 33rd Asilomar Conf. on Signals, Systems and Computers 1999, vol. 1. Multiple description source coding using forward error correction codes, (1999), pp. 342–346. https://doi.org/10.1109/acssc.1999.832349.
 39
W. Jiang, A. Ortega, in Proc. SPIE, vol. 3653. Multiple description coding via polyphase transform and selective quantization, (1999), pp. 998–1008. https://doi.org/10.1109/icassp.1999.760613.
 40
A. C. Miguel, A. E. Mohr, E. A. Riskin, in Proc. ICIP’99, vol. 3. SPIHT for generalized multiple description coding, (1999), pp. 842–846. https://doi.org/10.1109/icip.1999.817251.
 41
T. Tillo, G. Olmo, A novel multiple description codinig scheme compatible with the JPEG 2000 decoder. IEEE Signal Process. Lett.12(4), 329–332 (2005).
 42
J. Zong, L. Meng, H. Zhang, W. Wan, JNDbased multiple description image coding. KSII Trans. Internet Inf. Syst.11(8), 3935–3949 (2017).
 43
J. Zong, L. Meng, Y. Tan, J. Zhang, Y. Ren, H. Zhang, Adaptive reconstruction based multiple description coding with randomly offset quantizations. Multimed. Tools Appl.77(20), 26293–26313 (2018).
 44
C. Dai, K. Zhu, R. Wang, B. Chen, Contextual multiarmed bandit for cacheaware decoupled multiple association in UDNs: a deep learning approach. IEEE Trans. Cogn. Commun. Netw.5(4), 1046–1059 (2019).
 45
L. Zhao, H. Bai, A. Wang, Y. Zhao, Multiple description convolutional neural networks for image compression. IEEE Trans. Circ. Syst. Video Technol.29(8), 2494–2508 (2019).
 46
H. Li, L. Meng, J. Zhang, Y. Tan, Y. Ren, H. Zhang, Multiple description coding based on convolutional autoencoder. IEEE Access. 7:, 26013–26021 (2019).
 47
H. Zhang, H. Bai, M. Liu, Y. Zhao, Optimized multiple description lattice vector quantization coding for 3D depth image. Ksii Trans. Internet Inf. Syst.9(3) (2015).
 48
H. Bai, W. Lin, M. Zhang, A. Wang, Y. Zhao, Multiple description video coding based on human visual system characteristics. IEEE Trans. Circ. Syst. Video Technol.24(8), 1390–1394 (2014).
 49
J. Chen, C. Cai, X. Wang, H. Zeng, K. K. Ma, in Proceedings of the 16th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2015, vol. 9386. Multiple description coding for multiview video (Springer, 2015), pp. 876–882. https://doi.org/10.1007/9783319259031_75.
 50
J. Chen, J. Liao, H. Zeng, C. Cai, K. K. Ma, An efficient multiple description coding for multiview video based on the correlation of spatial polyphase transformed subsequences. J. Imaging Sci. Technol.63:, 50401–1504017 (2019).
 51
M. Liu, C. Zhu, Index assignment for 3description lattice vector quantization based on A_{2} lattice. Signal Process.88(11), 2754–2763 (2008).
 52
S. N. Talbar, A. K. Deshmane, in Proc. of 2010 International Conference on Computer Applications and Industrial Electronics. Biomedical image coding using dual tree discrete wavelet transform and noise shaping algorithm, (2010), pp. 473–476. https://doi.org/10.1109/iccaie.2010.5735126.
Acknowledgements
This work is supported by the National Natural Science Foundation of China under grant no. 61801167 and the Fundamental Research Funds for the Central Universities under grant no. B200202189.
Funding
This work is supported by the National Natural Science Foundation of China under grant no. 61801167 and the Fundamental Research Funds for the Central Universities under grant no. B200202189.
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Intra and intercorrelation in the index assignment of 3DLVQ mapping has been analyzed, as well as the intrasubband correlation exhibited in the lowfrequency wavelet subbands. In the context of wavelet image coding, a predictive side decoding method is proposed to improve reconstruction quality in side decoding. The author read and approved the final manuscript.
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Correspondence to Yuanyuan Xu.
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Xu, Y. Predictive side decoding for humancentered multiple description image coding. J Wireless Com Network 2020, 93 (2020). https://doi.org/10.1186/s1363802001719z
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Keywords
 Image coding
 Humancentered computing
 Multiple description coding
 Lattice vector quantization