A DCVS Reconstruction Algorithm for Mine Video Monitoring Image Based on Block Classification

Transcription

1 Artcle A DCVS Reconstructon Algorthm for Mne Vdeo Montorng Image Based on Block Classfcaton Xaohu Zhao 1,, Xueru Shen 1,, *, Kuan Wang 1, and Wanme L 1, 1 The Natonal and Local Jont Engneerng Laboratory of Internet Applcaton Technology on Mne, Xuzhou 1000, Chna; TS A3@cumt.edu.cn Chna Unversty of Mnng and Technology, Xuzhou 1000, Chna; TS P3@cumt.edu.cn * Correspondence: TS P3@cumt.edu.cn; Tel.: Abstract: Amng at the problems that large amount of vdeo montorng mage data n underground coal mnes leads to dffcultes n transmsson and storage, compressed sensng theory s ntroduced to encode and decode vdeo mages, and a new dstrbuted vdeo codng scheme s proposed. In order to obtan more sparse representaton and more general applcablty, a block-based adaptve sparse base scheme s proposed. For the acquston of sde nformaton, fxed weght s usually used to synthesze sde nformaton and the correlaton between dfferent mage blocks s neglected, a block-based classfcaton weghted sde nformaton generaton scheme s proposed. Expermental results show that the block-based classfcaton codec scheme can make full use of nter-frame correlaton. Under the approprate samplng rate, the PSNR value of vdeo reconstructon ncreases, whch effectvely mproves the qualty of vdeo frame reconstructon. Keywords: compressed sensng; dstrbuted vdeo codec; sparse representaton; sde nformaton reconstructon Introducton Coal s an mportant basc energy source n Chna and plays a decsve role n the development of economc constructon [1]. As a non-renewable resource, the shallow coal mne resources are gradually mned and turned to the explotaton of deep coal seams, and the dangers that come wth t are also ncreasng. Mne smart montorng, coal seam dentfcaton and mneral detecton have hgh requrements on the qualty and real-tme performance of vdeo mages, especally n unattended workng areas n coal mnes []. The amount of data of the montorng mage s large, so the requrement for the devce n terms of mage transmsson and storage s hgh. The wreless sensor network (WSN) reles on low-power wreless sensor nodes, overcomes the dffcultes of wred transmsson wrng, ncomplete detecton nformaton, hgh deployment costs and poor flexblty, and s wdely used n the transmsson of nformaton n coal mnes. Due to the partcularty of underground coal mne workng envronment, a large number of sensor nodes need to be arranged downhole to montor varous sgnals such as gas concentraton, temperature, humdty, and mne quake sgnals. Therefore, the amount of data the sensor needs to process s huge. However, the node energy and transmsson bandwdth of the sensor are lmted, and the energy supply of the mne node s dffcult, so t s hard to apply a large amount of data collecton. How to reduce the amount of nformaton transmsson and energy consumpton of nodes s an urgent task to extend the lfe cycle of wreless network nodes, and data compresson s a data processng technology that can effectvely reduce the amount of data transmsson. Compared wth the tradtonal Nyqust samplng mode, Compressed Sensng [3-4] technology s devoted to searchng for sparse solutons of underdetermned lnear systems. The sgnal can be reconstructed 018 by the author(s). Dstrbuted under a Creatve Commons CC BY lcense.

2 of wth a samplng rate much lower than the Nyqust samplng rate [5], whch s sutable for sgnal processng. In the vdeo codec scheme, tradtonal vdeo codng such as MPEG/H.6X, there wll be a large amount of moton estmaton and moton compensaton [6], whch makes the encodng sde much more complex than the decodng sde, ths asymmetrc codng scheme s sutable for a stuaton where multple encodngs are encoded at one tme, such as broadcastng. However, more scenaros requre low complexty, low power consumpton, and low compresson rato at the encoder end. The dstrbuted vdeo codng (DVC) has low codng complexty and s sutable for use n scenaros requrng low complexty. In a dstrbuted vdeo codng scheme, the decoder performs moton estmaton and compensaton to fnd the correlaton between adjacent frames, whch makes the codng sde less complex than the decodng sde. When usng CS theory to reconstruct the sgnal, the encodng sde s less complex than the decodng sde. However, usng only the CS theory cannot provde us wth a suffcently low samplng rate, so we combne the CS theory and the DVC theory to form a new theory -- dstrbuted compressed vdeo sensng (DCVS) to make up for ths defcency. The DCVS technology allows ndependent encodng of multple statstcally related sgnals at the encoder and jont decodng at the decoder, whch elmnates the need for complex moton estmaton and compensaton at the encoder end, thereby reducng the complexty of the encoder. Combnng the compressed sensng theory and the characterstcs of dstrbuted vdeo codec, ths paper proposes a block-based adaptve sparse representaton and weghted sde nformaton reconstructon scheme. Frstly, the related theores of compressed sensng and dstrbuted vdeo codng are brefly ntroduced. Based on the correlaton between vdeo frames, a block-based classfcaton sparse representaton and classfcaton weghted sde nformaton reconstructon scheme s proposed. Expermentally, the reconstructed PSNR value and tme complexty are obtaned, the expermental results are analyzed to verfy the effectveness of the algorthm.. Research Background Theoretcal Analyss.1.Analyss of Characterstcs of Wreless Sensor Network and Vdeo Image n Underground Coal Mne Under the specal workng envronment of coal mnes, f we want to fundamentally solve the problem of safe and effcent producton of coal mnes, the coal mnng ndustry must shft from labor-ntensve to technology-ntensve, makng t a new ndustry, new busness model, and new model wth hgh-tech features, and take the road of smart, few people (unmanned) and safe mnng [7]. Under the transton of technology-ntensve, wreless sensor networks are wdely used n nformaton transmsson n coal mnes because of ther advantages such as low-power sensor nodes. The communcaton dstance between WSN nodes s lmted, and a large number of sensor nodes need to be arranged. However, the energy of the nodes s lmted. The more data the nodes send and receve, the greater the energy consumpton. But the downhole power supply n coal mnes s dffcult, so the data transmsson needs to be reduced to reduce energy consumpton. Large-scale deployment of sensor nodes wll cause dfferent nodes n the network to transmt smlar data, resultng n a large amount of data redundancy and reducng transmsson effcency. Therefore, t s very necessary to apply compressed sensng theory to deal wth vdeo mages n coal mnes. In the collecton of vdeo mages n underground coal mnes, the collected mages are mostly grayscale mages composed of black and whte. The hue s relatvely sngle, but the correlaton between adjacent mages s very strong, and there s a large amount of redundant nformaton between vdeo frames. Compressed sensng technology can compress mage nformaton better and reduce transmsson pressure... Compressed Sensng Theory The compressed sensng theory proposed by Donoho et al. s a novel samplng theory. From the perspectve of analog to dgtal converson, the compressed sensng theory provdes a method of samplng at a rate lower than the Nyqust samplng rate wthout dstorton recovery [8]. Dfferent

3 3 of from the tradtonal samplng theorem, the theory ponts out that the precondton for the sgnal sparse representaton s that the sgnal s sparse. The sgnal x s thus converted to other doman space, represented by the sparse sgnal y. Consder an mage sgnal x of length N whose transform coeffcent on some orthogonal base s expressed as: x (1) Where s the coeffcent vector of x on the orthogonal bass, the nonzero number of s K and K<<N, The sgnal x s called K-sparse sgnal. The lnear predcton of the sgnal x s performed usng an observaton matrx : M N (M<<N) that s ncoherent wth the orthogonal bass y x () Where y s the observaton value and s the observaton matrx. In ths way, M lnear predctons whch are much smaller than N are obtaned, and by solvng an optmzaton problem, the orgnal sgnals x can be reconstructed wth hgh probablty from these few projectons. It can be proved that such a projecton contans enough nformaton to reconstruct the sgnal. In ths theoretcal framework, the samplng rate s not determned by the bandwdth of the sgnal, but depends on the structure and content of the nformaton n the sgnal..3 Dstrbuted Vdeo Codng Theory Dstrbuted vdeo codng (DVC) s a specfc applcaton of dstrbuted source codng (DSC) n vdeo processng. DSC s the codec problem of dstrbuted sources that are related to each other n tme or space. The nformaton theory bass of DVC s Slepan-Wolf and Wyner-Zv theorem [9]. Slepan-Wolf theory descrbes the condtons that need to be satsfed for lossless codng of related sources. It s proposed that the related source "ndependent codng, jont decodng" can be the same compresson effcency as "jont codng, and jont decodng". The Wyner-Zv theory s supplement and development based on the Slepan-Wolf theory and dscusses the descrpton of the rate-dstorton functon WZ R ( D ) of the relevant source codng n the case of lossy codng. The X/ Y Wyner-Zv rate-dstorton functon shows the lower lmt of the code rate for dstrbuted codng under a dstorton constrant. In the case of lossy codng, the rate dstorton functon obtaned by usng the sde nformaton only at the decodng end and usng at the codec sde s consstent [6]. Generally, Wyner-Zv encodng can be equvalent to quantfyng sources and then performng Slepan-Wolf encodng. 3. Vdeo Image Sparsty Analyss and Compresson Codng Processng 3.1.Sparse Representaton of the Sgnal Block-predcton and DCT Mxture Sparse Bass From Fourer transform to wavelet transform to later mult-scale geometrc analyss, the purpose of the scentsts' research s to study how to provde a more concse and drect analyss method for sgnals n dfferent functonal spaces. All of these transformatons are desgned to explot the characterstcs of the sgnal and sparsely represent t, or to mprove the nonlnear functon approxmaton of the sgnal, and further studyng the degree of sparsty of the sgnal or the degree of

4 4 of energy concentraton of the decomposton coeffcents usng a set of bases n a certan space. From the perspectve of sgnal analyss, the Fourer transform s the bass for sgnal and dgtal mage processng. Wavelet analyss brngs sgnal and dgtal mage processng to a whole new feld. Mult-scale geometrc analyss s a new generaton of sgnal analyss tool followng wavelet analyss. It has many excellent features such as mult-resoluton, localzaton and mult-drectonalty. It s more sutable for processng mult-dmensonal sgnals such as mages. All these studes lad the foundaton for the theory of compressed sensng. In order to get a better sparse representaton, many methods have been studed. In the tradtonal DCVS framework, lnear or orthogonal transform bases such as dscrete cosne transform (DCT) or dscrete wavelet transform (DWT) are wdely used due to ther smple and effcent features. Tradtonal vdeo compresson or dstrbuted vdeo codng (DVC) technology based on moton estmaton and moton compensaton all rely on a hgh-cost mechansm, that s, percepton or samplng and compresson are not performed contnuously, whch leads to waste of resources. In other words, most of the collected raw vdeo data may be dscarded n the complex compresson process [10]. However, learnng-based dctonares can provde a more sparse representaton of mage sgnals than these pre-specfed base classes. A dctonary learned from a set of blocks globally extracted from the prevous reconstructed neghborng frames s used as sparse bass n [11]. Nevertheless, ths dctonary s based on K-SVD algorthm [1] and can be only appled n some specfc applcatons. Dctonares reported n [13] and [14] are generated by a lnear combnaton of neghborng blocks n precedng and followng key frames. However, wth lmted reference nformaton, the learned dctonary may not be suffcently redundant. In [15], a smple sde nformaton generaton technque based on correlaton analyss of CS measurements s effectve n complexty reducton. But the rate-dstorton performance s unsatsfyng compared wth other schemes. In the tradtonal vdeo codec, a hybrd codng method combnng predcton and DCT can quckly reduce spatotemporal correlaton and obtan a more sparse representaton of the vdeo frame. In order to obtan better rate-dstorton performance, we propose a new block predcton and hybrd sparse bass for non-key frames wth reference to ths hybrd codng method. As shown n equaton (3), the new sparse bass conssts of two parts, the ntal DCT matrx and the block-predcton bass. In ths way, ntal DCT bass s acqured by lnear transform n DCT doman whle block-predcton bass s based on the SI generated from the adjacent decoded key frames. ; (3) DCT nt er 16 Where N N DCT R s an ntal DCT bass, N 1 nt er R s a block-predcton bass and 163 s a newly bult hybrd DCT bass. N ( N 1) R Classfcaton Judgment Standard In order to obtan more sparse representatons and more general applcablty, ths paper proposes a new hybrd sparse scheme that combnes lnear DCT bases and block predcton bases to solve sparse representaton problems. Adaptve block-based predcton s used to generate sde nformaton and to learn block predcton bass. Snce the vdeo s composed of consecutve frames

5 5 of and the tme redundancy s partcularly large, that s, the nter-frame correlaton s very strong. By utlzng the correlaton between adjacent frames, the scheme can acheve more sparse representaton whle reducng the complexty. Dfferent regons n the vdeo sequence have dfferent nter-frame correlatons. We propose an adaptve block predcton scheme for sparse representaton. Perform the non-overlappng block processng of the reconstructon results of the two adjacent key frames before and after the current 175 non-key frame. The dfference between the -th block xt 1 of the followng frame and the -th block xt 1 of the prevous frame at the correspondng frame may reflect the correlaton between the nter-frames n the vdeo sequence. Consder the resdual energy values of the two correspondng frame sub-blocks as the classfcaton crtera, and defne the resdual energy value as shown n equaton (4): t1 t 1 t 1 t1 (4) E( x, x ) x x However, for vdeo frames where vdeo scenes change rapdly, the resdual energy value of the correspondng poston may be large. Therefore, smply usng the resdual energy value as the classfcaton crteron wll cause the threshold value to be too dependent on the sequence tself, makng the algorthm less versatle. Based on ths, t s thought that the rato of the resdual energy to the energy block of the prevous key frame s used as the classfcaton crteron, as defned by equaton (5): e( x, x ) E( x, x ) E( x ) t 1 t 1 t 1 t1 t 1 The vdeo frame sub-blocks are classfed usng thresholds T1, T (T1 < T) for vdeo sequences. Calculate the value of the judgment functon of the -th block accordng to equaton (5).If e( x, x ) T,then the block s defned as block wth slght movement; f T e( x, x ) T t1 t1 1 t 1 t 1 (5) 1 t 1 t 1,then the block s defned as block wth bg movement; and f e( x, x ) T, then the block s defned as block wth severe movement. In the experment, the vdeo frame s subjected to block classfcaton processng. The classfcaton judgment thresholds T1 and T take and respectvely. After block classfcaton of non-key frames, the classfcaton results and correspondng sparse base strateges are shown n Fgure 1.

6 6 of Fgure 1. Block classfcaton of non-key frames After the non-key frames are blocked accordng to the defned classfcaton crtera, dfferent sparse-based strateges are proposed for dfferent types of blocks. Algorthm 1 descrbes the process of adaptve hybrd DCT-based learnng. For the block wth slght movement, predctons are generated by generatng the sde nformaton by performng forward and backward moton estmaton on adjacent decoded key frames. The block predcton bass s learned and combned wth the DCT bass to construct a hybrd DCT bass. For the block wth bg movement, by selectng dfferent weghts and combnng forward and backward moton estmaton to obtan sde nformaton, the constructon method of the hybrd DCT base s smlar to the block wth slght movement. For the block wth severe movement, the nter-frame correlaton between adjacent frames s small, and moton estmaton and compensaton cannot accurately predct these fast-changng blocks. Therefore, for ths type of block, only the ntal DCT bass can be used. The descrpton of algorthm 1 s as follows. Algorthm 1: The hybrd DCT sparse bass algorthm based on block predcton. Input: xt 1, xt 1 :Neghborng decoded key frames as reference for the -th non-key frames. 1. Calculate the resdual energy value of the correspondng frame sub-block: t1 t 1 t 1 t1 ; E( x, x ) x x. Calculate the rato e( x, x ) E( x, x ) E( x ) t 1 t 1 t 1 t1 t 1 of the resdual energy to the energy block of the prevous key frame as a classfcaton crteron; 3. Calculate sde nformaton SI x ( 1 ) x, Where selects dfferent weghts accordng to the classfcaton; t1 t1 4. If e( xt 1, xt 1) T1, 0. 8, nt er x / 55, ( ) ; 5. If T1 e( xt 1, xt 1) T, 0. 5, nt er x / 55 t t ; t DCT nt er, ( ) ; ;otherwse, t DCT nt er

7 7 of , ( ). t DCT Output: ( ) t :Hybrd DCT sparse bass for the t-th block n the -th non-key frame Desgn of Observaton Matrx After the sparse representaton, how to desgn a stable M N dmensonal observaton matrx Φ that s not related to the transform bass Ψ, and ensure that the mportant nformaton of the sparse vector when descendng from the N dmenson to the M dmenson s not destroyed, ths s the second problem to be solved. In compressed sensng theory, after the sparse coeffcent vector T X of the sgnal s obtaned by transformaton, the observaton part of the compressed sample s desgned. The purpose of the observaton matrx desgn s how to sample and obtan M observatons, and t can be ensured that a sgnal X of length N or a sparse coeffcent vector Ψ of the base Ψ can be reconstructed from t. The mportance of the observaton matrx desgn s that f the sgnal X s destroyed durng the observaton process, t s mpossble to reconstruct sgnal successfully. The M observaton process actually uses the M row vectors of the M N dmensonal observaton matrx Φ to project the sparse coeffcent vector. That s, to calculate the nner product between θ and each observaton vector j M j 1 j j 1, and get M observatons y j, j ( j 1,,..., M ), for the observaton vector Y ( y 1, y,..., y M ), that s T CS Y X A X (6) The samplng process here s adaptve, that s, θ does not have to change accordng to the change of X, and the observed data s no longer a pont samplng, but a more general K lnear functonal of the sgnal. Gven a vector Y, fndng θ from equaton (6) s a lnear programmng problem. However, snce M<<N, that s, the number of equatons s less than the number of unknowns, ths s an underdetermned problem and the determnstc soluton cannot be calculated. However, f θ has K-sparseness (K<<M), t s expected to fnd a certan soluton just try to determne the approprate poston of the K non-zero coeffcents n. Snce the observaton vector Y s a lnear combnaton of K non-zero coeffcent column vectors, a lnear equaton of M N s formed to solve the specfc values of these non-zero terms. For whether there are necessary and suffcent condtons for determnng the soluton, Restrcted Isometry Property (RIP)[16] gves the defnton, The necessary and suffcent condtons for the defnton are consstent wth the geometrc propertes proposed by Candes and Tao et al that the sparse sgnals must mantan under the observaton matrx. In the reconstructon process of the sgnal, n order to completely reconstruct the sgnal, t must be ensured that the observaton matrx does not map two dfferent K-term coeffcent sgnals nto the same samplng set, whch requres that the matrx formed by the M column vectors extracted from the observaton matrx s non-sngular. So the key to solvng such problems s how to determne the poston of the non-zero coeffcents to form a lnear system of equatons. CS How to judge the RIP property of a gven matrx A s a combnaton complexty problem. Fndng an alternatve method that can easly mplement RIP characterstcs s the key to successfully constructng the observaton matrx. If the observaton matrx θ and the sparse bass CS are guaranteed to be uncorrelated, A can largely satsfy the RIP property. Irrelevant means that the vector j cannot be sparsely represented by. The stronger the rrelevance, the more coeffcents are needed to represent each other; on the contrary, the stronger the relevance. j

8 8 of Candes E J et al. proved that when Φ s a Gaussan random matrx, the ncoherence and RIP condtons can be satsfed wth a large probablty. Therefore, the observaton matrx Φ n ths paper adopts a random Gauss matrx. A random Gaussan matrx has a useful property: for a T CS M N random Gaussan matrx Φ, t can be shown that when M ck log( N / K), A satsfes the RIP property wth a large probablty (where c s a small constant). Therefore, a K-term sparse sgnal length of N can be reconstructed wth hgh probablty from M observatons. The random Gaussan matrx s not related to the matrx formed by most fxed orthogonal bases. Ths property determnes the choce that we choose t as an observaton matrx. When other orthogonal bases are used as the sparse transform base, the RIP can be satsfed Compressed Codec Processng Research Compressed sensng s a new way of sgnal acquston that allows us to desgn very smple vdeo encodngs that can be mplemented on moble devces wth lmted resources. However, the CS-based vdeo codec proposed by the predecessors ether requres a conventonal vdeo encoder or a feedback channel, whch ncreases the complexty of the codec. The dstrbuted compressed vdeo sensng (DCVS) codec scheme proposed n ths paper uses CS only at the encodng end, and the decoder uses a correlaton between CS measurements of adjacent frames to form a new sde nformaton generatng scheme. The new DCVS block dagram we proposed s shown n Fgure. Ths codec s completely CS-based and does not nvolve tradtonal vdeo encoders. Both key and non-key frames are encoded as CS measurements and no feedback channel s requred Fgure. DCVS vdeo codec scheme At the encodng end, the vdeo sequence s dvded nto a number of Group of Pctures (GOPs). Vdeo frames are dvded nto key frames and non-key frames, non-key frames also called Wyner-Zv (WZ) frames, whch form a group of pctures. Each GOP contans one key frame and several non-key frames. Both key frames and non-key frames are coded wth smlar CS theory. At the encodng end, each key frame s reconstructed by the SAMP algorthm, whch redefnes the l1 mnmzaton problem, as shown n equaton (7). 1 mn yk A k k (7) 1 k 80 Where y k s the CS measurement of the key frame obtaned at the decodng end, A has 81 N been descrbed n the prevous, R 1 s the sparse coeffcent vector of the soluton. Key k 8 frame x ˆk s obtaned by x ˆk ˆ k, where ˆk s the optmal soluton of k n equaton (7) The decodng of non-key frames are asssted by the sde nformaton generated by the dctonary and the sde nformaton s generated by the nverse quantzed CS measurement of the key frame. The effect of sde nformaton s only apparent when there s suffcent correlaton between the measured values of the key frame and the WZ frame. In a vdeo sequence, adjacent

9 9 of frames n the same scene are hghly correlated. Therefore, we assume that even f the CS measurement process s very dfferent from a lnear transformaton (such as DCT), the CS measurement of such adjacent frames s hghly correlated. On the one hand, the DCT coeffcents follow a Laplacan dstrbuton. On the other hand, CS measurements follow a more or less normal (Gaussan) dstrbuton. 4. DCVS Framework Based on Block Classfcaton Weghted Sde Informaton 4.1. Descrpton of the Framework In the DCVS framework, n order to mprove the reconstructon qualty of non-key frames at the decodng end, sde nformaton s ntroduced when reconstructng non-key frames. Therefore, the acquston of sde nformaton plays a very mportant role n the DCVS framework. Especally for the reconstructon of non-key frames, f the acquston of sde nformaton s not accurate, the performance of ts reconstructon wll be greatly affected. The process of obtanng the sde nformaton may be generated by usng a K-SVD tranng dctonary or performng moton estmaton on the decoded key frame, nterpolatng n the tme doman to generate sde nformaton. Most of the tradtonal methods of obtanng the sde nformaton are to estmate the forward and backward moton of the reconstructed values of the two key frames before and after the current frame, then accordng to a fxed weght (usually 1/), addng to obtan the sde nformaton to assst n the reconstructon of non-key frames [17]. However, the nter-frame correlatons of dfferent regons n the vdeo sequence are dfferent, and the varyng scenes and moton levels of the vdeo are dfferent. If the nter-frame correlaton between the forward and backward moton estmaton s weak, stll usng a fxed weght does not predct the current frame very well, so the accuracy of the generated sde nformaton s not hgh, whch n turn affects the reconstructon of non-key frames. Therefore, the tradtonal method of syntheszng sde nformaton by usng fxed weght 1/ does not make good use of nter-frame correlaton. Accordng to the correlaton between dfferent blocks of vdeo frames, a DCVS framework based on dfferent blocks s proposed. The framework s shown n Fgure Fgure 3. Block-based classfcaton weghted sde nformaton DCVS framework At the encodng end, the current frame and the two key frames are subjected to block sparse representaton and CS samplng measurement. The key frames are sampled by the tradtonal DWT algorthm, the non-key frames are sampled by the hybrd sparse algorthm, and the measurement matrx stll selects the random Gauss matrx. At the decodng end, the key frame s frst reconstructed by SAMP algorthm, and then the classfcaton judgment method ntroduced n the prevous secton s used to classfy non-key(cs) frames, performng forward and backward moton

10 10 of estmaton on reconstructed values of adjacent key frames, dfferent weghtng schemes are used to generate sde nformaton accordng to dfferent block categores. And the reconstructon of non-key frames requres the use of sde nformaton n conjuncton wth resdual decodng of non-key frames. 4.. Non-key Frame Reconstructon The reconstructon of non-key frames uses the moton estmaton of the classfcaton weghts to generate the sde nformaton, and then uses the sde nformaton and the measured values of the current frame to reconstruct the resdual of the current frame. The reconstructon result of the non-key frame s the combnaton of the sde nformaton and the resdual reconstructon. Assumng that the current frame of a vdeo s x and the predcted value of the current frame s x, the resdual between the actual result and the predcted value of the current frame s r x x, the predcted value x of the current frame s generated by predctng the current frame from the pre- and post-decodng key frames. Snce the decodng end has not yet obtaned the reconstructed data of the decoded key frames before and after the current frame, so we convert the resdual to the measurement doman: d r x x y x (8) If the dfference between the actual value and the predcted value of the current frame s smaller, the smaller the resdual of the two s, the sparser t s, the smaller the samplng under the measurement matrx s, and the better the reconstructon effect of the resdual s. The reconstructon result of the current frame s: (9) Where rec frame, and x x r rec rec x s the reconstructon result of the current frame, x s the predcted value of the current r s the result of the resdual reconstructon. rec In the acquston of sde nformaton, t s necessary to utlze the reconstructon result of adjacent decodng key frames [18]. Frst, forward and backward moton estmaton s performed on the reconstructon results of the -th blocks x 1 and x 1 correspondng to the adjacent decodng t key frames to obtan xˆ t 1 and xˆ t 1 respectvely, then fnd the sde nformaton of the -th block accordng to equaton (10): SI xˆ ( 1 ) xˆ (10) t t1 t1 Where s the weght coeffcent. In the prevous secton, the non-key frame blocks have been classfed and judged. After the blocks of dfferent regons are classfed, dfferent weghts are adopted to obtan the sde nformaton, thereby further reconstructng the non-key frames. For the block wth slght movement, the rato of the resdual energy to the energy block of the prevous key frame s small, and the nter-frame correlaton of the precedng and succeedng frames s large. Accordng to equaton (5), the forward moton estmaton can predct the current frame very well, and then take to a larger value. Conversely, for the block wth severe movement, the nter-frame correlaton s small, and the backward moton estmaton can better predct the current frame, and then take to a smaller value. For the block wth bg movement, the nter-frame correlaton s moderate, and the current frame can be predcted by weghted averagng of forward and backward moton estmaton. Based on the above analyss, the weghts of the block wth slght, bg and severe movement are 0. 7, 0. 5, respectvely set to In summary, for the reconstructon process of non-key frames summarzed as follows: Frstly, the adjacent key frames are reconstructed, and the reconstructon results are classfed and judged, and the forward and backward moton estmaton s performed on adjacent key frames. Combned wth the classfcaton crtera and forward and backward moton estmaton, the sde nformaton of the non-key frame s obtaned accordng to the equaton (10). Accordng to the moton estmaton, the sde nformaton and the current frame are obtaned, and the resdual reconstructon of the

11 11 of non-key frame resdual s performed. The reconstructon result of the non-key frame s the sum of the sde nformaton obtaned by the moton estmaton and the non-key frame resdual reconstructon. Algorthm : The descrpton of the non-key frame reconstructon algorthm. Input: y,, x, x s t1 t1 1. Perform forward and backward moton estmaton for the pre- and post-decodng key frames xt 1 and xt 1 respectvely to obtan xˆt 1 and xˆt 1 ;. Accordng to the classfcaton judgment result of xt 1 and x t1, the sde nformaton s obtaned by the formula SI x ( 1 ) x, Where selects dfferent weghts t 1 t1 dependng on the classfcaton; 3. Calculate the resdual of the measured value y and the SI n the measurement doman: r y cs _ Encoder( SI, ) ; 4. Reconstruct the resdual r to x r ; 5. Reconstructon results for non-key frames: t r s x SI x. Output: x t :Reconstructon of the t-th block non-key frame Smulaton Experment Results and Analyss The experment selects two sets of vdeo sequences collected n the underground coal mne to test the performance of the classfed weghted sde nformaton method under hybrd sparse bass. In the experment, the classfcaton weghted sde nformaton generaton method and the fxed weght sde nformaton generaton method under the hybrd sparse bass are compared wth the orgnal DCVS algorthm. The reconstructon algorthm n the classfcaton weghted sde nformaton selects the SAMP algorthm. The measurement and reconstructon of key frames and non-key frames n the vdeo sequence are block-based. We defne the frst frame as the key frame. Snce the key frame has a great nfluence on the generaton and reconstructon of the sde nformaton, so we choose a key frame wth a samplng rate of 0.9. The non-key frame samplng rate s selected by comparng the PSNR values reconstructed by the classfed weghted sde nformaton and the fxed weghted sde nformaton at dfferent samplng rates. Select two dfferent vdeo scenes, and the comparson results of PSNR values at dfferent samplng rates are shown n Fgure 4. The classfcaton thresholds T1 and T are: T1=0.003,T=0.015 In order to verfy the effect of the algorthm, by comparng the tme complexty of the algorthm under dfferent samplng rates, the peak sgnal-to-nose rato PSNR value s used to objectvely evaluate the reconstructon effect of the algorthm. To calculate the PSNR value, the value of the mean square error MSE s frst calculated. The MSE s defned as: m n 1 MSE 1 1 I (, j) K(, j) (11) mn 0 j0 The peak sgnal to nose rato PSNR s defned as: n ( 1) PSNR 10 log 10( ) (1) MSE It s not dffcult to understand that t can be obtaned from equaton (1) that the smaller the value of the MSE s, the closer the reconstructed vdeo frame effect s to the orgnal vdeo frame mage, and the larger the PSNR value s.

12 1 of DCVS.8.6 DCVS Tme/s Tme/s Samplng Rate Samplng Rate Fgure 4. Relatonshp between samplng rate and tme complexty of vdeo sequences PSNR/dB 6 5 DCVS PSNR/dB 7 6 DCVS Samplng Rate Samplng Rate Fgure 5. PSNR value of vdeo reconstructon qualty at dfferent samplng rates As can be seen from Fgure 4, the complexty of the proposed algorthm s smlar to the fxed-weghted sde nformaton algorthm at dfferent samplng rates. The orgnal DCVS algorthm has the shortest runnng tme, that s, the complexty of the algorthm s lower than the former two. However, as can be seen from Fgure 5, as the samplng rate ncreases, the PSNR value ncreases correspondngly. The reconstructon effect s relatvely good under the condton of hgh samplng rate. Obvously, the proposed algorthm has the hghest reconstructon qualty. Although the complexty of ts reconstructon s relatvely hgh, ts complexty s wthn an acceptable range relatve to the mprovement of reconstructon qualty. Fgure 5 shows that when the samplng rate s greater than 0.3, the ncrease of the PSNR value s not large as the samplng rate ncreases. Consderng that, n the case of meetng the general needs, the low samplng rate requres less transmsson data. Therefore, the samplng rate of non-key frames s selected to be 0.3, and the lesser transmsson data requred at low samplng rates saves energy whle ensurng reconstructon qualty. 8 7 DCVS 6 PSNR/dB GOPSze Fgure 6. PSNR values of vdeo reconstructon qualty under dfferent GOP szes

13 13 of 15.1 DCVS 1.9 Tme/s GOPSze Fgure 7. Tme complexty under dfferent GOP szes As can be seen from Fgure 6 and Fgure 7, as the number of frames n each GOP ncreases, the nter-frame correlaton becomes smaller, resultng n a decrease n the reconstructed PSNR value. However, the reconstructon effect of the proposed algorthm has always been above the fxed weght sde nformaton and the orgnal DCVS algorthm, and ts reconstructon complexty s close to the orgnal DCVS algorthm, whch saves energy consumpton whle ensurng the reconstructon qualty. Experment to select the 30 GOPs of the vdeo sequence, there are 3 frames n each GOP, and a total of 90 frames are respectvely smulated. The two frames before and after the GOP are taken as key frames, and the ntermedate frames are non-key frames. Through sparse representaton, sde nformaton generaton, frame reconstructon, the qualty of the two sde nformaton generaton method s compared between the classfcaton weghted sde nformaton and the fxed weght sde nformaton, and s objectvely measured by the PSNR value. The curve of the reconstructed qualty PSNR value of 30 frames of non-key frames n the vdeo sequence GOP s shown n Fgure PSNR/dB PSNR/dB Fgure 8. PSNR value of non-key frame reconstructon (samplng rate 0.3) It can be seen from the fgure that under the same condtons, the PSNR value of the block-based classfcaton adaptve sparse bass and the classfcaton weghted sde nformaton s mproved by dB compared wth the fxed weght sde nformaton reconstructon. For mage blocks wth too hgh movement ntensty, the correlaton between adjacent frames s weak, and the reconstructon effect s relatvely weak, but the reconstructon effect s stll mproved compared wth the fxed weght sde nformaton. Therefore, the proposed algorthm can mprove the qualty of vdeo reconstructon n dfferent vdeo scenaros. 6. Conclusons N-th frame N-th frame In order to solve the problem that the correlaton between the dfferent vdeo sequence mage blocks s dfferent n the process of applyng the compressed sensng method to the vdeo decodng sde to acqure the sde nformaton for assstng non-key frame reconstructon, a block-based

14 14 of classfcaton weghtng method s proposed for non-key frame reconstructon. In terms of the sparse representaton of the encodng end, the vdeo sequence s dvded nto dfferent blocks accordng to the nter-frame correlaton, and dfferent sparse bass strateges are selected for dfferent blocks; In the decodng end of the non-key frame, n the algorthm for generatng the sde nformaton by moton estmaton, the generaton of the SI selects dfferent weghts accordng to the dfference of the nter-frame correlaton. The expermental results show that the scheme can adaptvely select dfferent sparse bass and sde nformaton generaton schemes to assst non-key frame reconstructon accordng to dfferent vdeo scenes, makng full use of nter-frame correlaton and mprovng reconstructon qualty. Author Contrbutons: Conceptualzaton, K.W. and M.W.; Methodology, R.X.; Software, R.X.; Valdaton, R.X., K.W. and M.W.; Formal Analyss, R.X.; Resources, K.W.; Wrtng-Orgnal Draft Preparaton, R.X.; Wrtng-Revew & Edtng, H.X.; Vsualzaton, R.X.; Supervson, H.X.; Project Admnstraton, H.X.; Fundng Acquston, H.X. Fundng: Ths work s supported by the Natonal Key Research and Development Project of Chna (No.017YFC ). Conflcts of Interest: The authors declare no conflct of nterest. References 1. Zhao, X.H.; Deng, Y.F.; Mu, D.C. Appled study on compressed sensng technology to mne Internet of thngs. Coal Scence and Technology, 016, 44(7), 69-7[CrossRef].. Zhao X.H.; Lu S.S.; Shen X.R.; et al. Mcro-sesmc data compresson and reconstructon based on dstrbuted compressed sensng. Journal of Chna Unversty of Mnng & Technology, 018, 1, 17-18[CrossRef]. 3. Zhang F.; Yan X.X.; L Y.J. A novel mage reconstructon method of mne ntellgent survellance based on adaptve sparse representaton. Journal of Chna Coal Socety, 017, 4(5), [CrossRef]. 4. Zhang F.; Yan X.X. The block compressed sensng of mne montorng mages based on DFT bass. Journal of Transducton Technology, 017, 30(1), [CrossRef]. 5. Wang G.; Zhao Z.K.; Nng Y.J. Desgn of Compressed Sensng Algorthm for Coal Mne IoT Movng Measurement Data Based on a Mult-Hop Network and Total Varaton. SENSORS, 018, 18(6),173[CrossRef] 6. Ln B.L.; Zheng B.Y.; Qan C. The reconstructon methods of frames classfcaton of dstrbuted vdeo compresson codng. Sgnal Processng, 015,, 01-07[CrossRef]. 7. Zhao X.H.; Lu S.S.; Shen X.R.; et al. Research on processng algorthm of mage n underground coal mne based on CS framework. Coal Scence and Technology, 018, 46(), 19-4[CrossRef]. 8. Zhang R. A prelmnary study of mage reconstructon and denosng based on compressed sensng. Southwest Jaotong Unversty, 010[CrossRef]. 9. Wu M.H.; Zhu X.C. Dynamc measurement rate allocaton for dstrbuted compressve vdeo sensng. Journal of Nanjng Unversty of Posts and Telecommuncatons, 013, 33(1), 6-67[CrossRef]. 10. Dong H.; Zhuang B.; Su F.; et al. A novel dstrbuted compressve vdeo sensng based on hybrd sparse bass. // Vsual Communcatons and Image Processng Conference. IEEE, 014, 30-33[CrossRef]. 11. Chen H.W.; Kang L.W.; Lu C.S. Dctonary learnng-based dstrbuted compressve vdeo sensng.// Pcture Codng Symposum. 011, 10-13[CrossRef]. 1. Aharon M.; Elad M.; Brucksten A. rmk-svd: An algorthm for desgnng overcomplete dctonares for sparse representaton. IEEE Transactons on Sgnal Processng, 006, 54(11), [CrossRef]. 13. Prades N.J.; Ma Y.; Huang T. Dstrbuted vdeo codng usng compressve samplng.// Pcture Codng Symposum. IEEE, 009, 1-4[CrossRef]. 14. Lu L.; Wang A.; L Z.; et al. An Improved Dstrbuted Compressve Vdeo Sensng Based on Adaptve Sparse Bass. // Internatonal Conference on Robot. IEEE, 011, [CrossRef]. 15. Bag Y.; La E.M.K.; Punchhewa A. Dstrbuted Vdeo Codng Based on Compressed Sensng.//IEEE Internatonal Conference on Multmeda and Expo Workshops. IEEE, 01, 131(5), [CrossRef]. 16. Baranuk R.; Davenport M.; Devore R.; et al. A Smple Proof of the Restrcted Isometry Property for Random Matrces.// CONSTR APPROX, 008, 53-63[CrossRef].

15 15 of Da Y.Y.; Cao X.Q.; Chen R.; et al. Reconstructon algorthm wth classfed weghted sde Informaton for dstrbuted vdeo compressve sensng. Computer Technology and Development, 017, 7(5), 87-91[CrossRef]. 18. Jan C.; Su K.X.; Wang W.X.; et al. Resdual Dstrbuted Compressve Vdeo Sensng Based on Double Sde Informaton. Acta Automatca Snca, 014, 40(10), [CrossRef].