CN101312544A - Video quality automatic evaluation system oriented to wireless network and evaluation method thereof - Google Patents

Abstract

The invention relates to a video quality automatic evaluation method and an evaluation method, relating to the evaluation of wireless video communication, which can confirm the evaluation property, reduce needed reference data and reduce calculation complexity, therefore, the evaluation mode is adaptive to the evaluation application facing wireless network. The method comprises: processing content pre-check on the reference video not compressed; according to the average activity of each activity region, dividing the check on the video characters into high and low activity region character checks, to attain the check parameters representing the spatial domain and time domain characters which express the characters of the video sections in each activity region; weighting the check parameters of the spatial domain and time domain characters of each activity region to build the spatial domain and time domain character parameters of the whole video, realizing the projection between the check parameters and the subjective quality expression according to a preset video quality evaluation database, to realize the automatic evaluation on the video quality facing wireless network.

Description

Video quality automatic evaluation system and evaluating method thereof towards wireless network

Technical field

The present invention relates to a kind of evaluation and test of wireless video communication, especially relate to a kind of evaluating method of the video quality automatic evaluation system towards wireless network.

Background technology

The overall development trend of communication network is: " broadband ", " wireless penetration " and " mobile ".Along with the development of broadband wireless communication technique and popularizing of Internet business, wireless network loaded service form is also abundant day by day, presents " multimedization ", " entertainment orientation " and development trends such as " personalizations ".In the following several years, conventional telecommunications will progressively be replaced by the multimedia service based on video based on the business form of speech (or low discharge data) on the net.People will no longer content just to by wireless network transmissions literal and sound, also wish to see by wireless network the picture and the scene of life, will become the main body business of following wireless network such as Video service such as mobile phone movie theatre, mobile TV, mobile network game, mobile video session, radio multimedium mails.Automatic evaluation and test at the wireless video quality is the important technology that runs through whole wireless video communication " design ", " sale ", " application " three big links, is embodied in following three aspects:

At first, at system design stage, research is fit to the main direction that video communication system that wireless transmission uses, code encoding/decoding mode, communication protocol etc. will become the system development design, just will be from the application of traditional " towards storage " the application transformation to " towards transmission " as the encoding and decoding technique of video of new generation (or image).New departure performance of research and development presses for relevant wireless video quality assessment technology so that objective, accurate, just judge to be provided, thereby helps the research staff to carry out the improvement and the optimization of performance.Therefore, at system design stage, the wireless video quality assessment has mainly embodied the application demand of " optimization purpose ".

Secondly, in the system sales stage, growing along with the wireless video business, its huge market share will attract the wireless video product of different manufacturers, different technologies, difference in functionality to pour in market.Prove that the performance difference between different product just must provide quantifiable, objective, just wireless video quality assessment mechanism, thus convenient comparison and popularization to the different product cost performance.Provide convictive wireless video communication quality assessment result to help pushing the sale of one's products, promoting the image of own product.Therefore in the system sales stage, the wireless video quality assessment has mainly embodied the application demand of " selection purpose ".At last, in the system applies stage, will cause the classifying appearance of service mechanism of the development characteristic of " personalization " of following radio communication service is bought wireless video service quality preferably as the user, just need provide higher rate to the service supplier.No matter be that service supplier or client urgently wish oneself to provide or the wireless video quality of service bought can obtain matched repayment.By the monitoring to the wireless video quality, whether the service that the service supplier can in time monitor on the one hand the user that offers meets promise originally; When the user proposes doubtful point to its payment quality, can provide objective, convictive evidence on the other hand to safeguard the rights and interests of self.Therefore, in the system applies stage, the wireless video quality assessment has mainly embodied the demand of " monitoring purposes ".From the theoretical system angle, the video quality objective evaluating method can be divided into three major types, and promptly full reference hierarchy (FR, FullReference), partial reference system (RR, Reduced Reference) and no reference hierarchy (NR, No Reference).From the angle of wireless application, the FR system is fit to be applied in " off-line " evaluation and test field that can obtain the complete reference material; And RR and NR system are fit to be applied in " online " evaluation and test field that can not obtain the complete reference material.Between three more than ten years in the past, the researcher is being devoted to the research of relevant evaluation and test algorithm always, and proposed different theories and method (1, Tong Yubing, Hu Weiwei. method for evaluating video quality summary [J]. computer-aided design and graphics journal, 2006,18 (5): 735-741; 2, Yuan Fei, Huang Lianfen, Yao Yan. video quality objective assessment technical research [J]. TV tech, 2007,31 (3): 91-94), yet up to the present its achievement not significantly (3, VQEG.Final report from VQEG on the validation of objectivemodels of video quality assessment[S], 2000; 4, VQEG Final report from VQEG on the validationof objective models of video quality assessment, phase II[S], 2003; 5, VQEG.RRNR-TV GroupTest Plan Version 1.7[EB/OL] .2006; 6, ITU-T Objective perceptual video qualitymeasurement techniques for digital cable television in the presence of a full reference[S J.144], 2004; 7, ITU-R Recommendation BT.500-8, Methodology for the subjective assessment of the quality oftelevision pictures[S], 1998; 8, ITU-R BT.1683 Objective perceptual video quality measurementtechniques for standard definition digital broadcast television in the presence of a full reference[S], 2004).Current also do not have the solution that can reach common understanding on the problem of machine quality evaluation, do not have so-called preferred option yet.It in fact all is human further announcement to the cognitive principle of self visually-perceptible quality that each algorithm of researcher is broken through.Conclusion is got up, and the digital video particularly main difficult point of the automatic evaluation and test of wireless video quality has following three aspects:

At first, digital video is wireless video particularly, because the quality deterioration presentation of initiations such as information source compression and chnnel coding substantially exceeds the type of distortion of traditional analog mode.Different information source code encoding/decoding modes and Channel Transmission condition all can cause the different deterioration distortion that can not expect, therefore are difficult to its types of degradation and presentation are carried out complete conclusion and accurately prediction.

Secondly, the design object of compression of digital video has essential difference with simulation algorithm, and it is not an integrality of making every effort to keep original signal waveform, but approaches primary signal on visual effect; And in wireless transmission, be subjected to bandwidth constraints often to need to remove visual redundancy information according to human vision property.The mass change that above-mentioned information source and channel are caused as weigh with traditional wave-form similarity then lose biased, thereby conventional needle to the quality assessment method of analog video signal quality and be not suitable for digital video particularly the evaluation and test of wireless video use.

In addition, the biggest obstacle of research is still understood not clear to the mankind's cognitive mechanism.Perception and understanding that these not only relate to the human vision excitation also relate to interactional behavior rule between them.People's vision is a very complex physical system, its mechanism is not understood as yet fully, and people's sensation can be by about all non-technical factors institute, as environment, position, scene and taste etc., and these factors are difficult to accurately measure and quantize, though therefore evaluation model is numerous, the result is barely satisfactory.

From present technology development situation, be to be fit to the evaluation and test system that wireless evaluation and test is used based on the reference compression (RR, Reduced Reference) of feature extraction.Its core method of testing is earlier material to be measured necessarily to be handled at transmitting terminal, extracts the key character information of being paid close attention to, and these information independently are delivered to receiving terminal with video file; Then extract the above-mentioned characteristic information of material to be measured at receiving terminal, and the characteristic information that extracts is compared with transmitting the characteristic information that comes, thereby determine transmission quality with same processing mode.

Yet there are two kinds of study limitations in existing RR class methods: one, regard the video file equivalence image collection of N frame as, and the every two field picture at video detects respectively, and the quality score of N image is asked on average, as the comprehensive quality of this video.They are two years old, regard video file as imformosome that time domain and spatial domain are separated, adopt mechanical, single, fixing spatial domain and the time area detecting method, ignore influence characteristic and the rule of video content characteristics (as activity) to quality, and computational process is independently to carry out, and is inharmonious mutually.

In above-mentioned two kinds of research thinkings, the former ignores the time domain Frequency Response of HVS, mechanically will detect at the detection method of the rest image video quality of extending, and its essence is that the machinery of rest image quality testing on time domain extends; The latter has considered the influence of time-domain response characteristic to the video evaluation and test, but that its processing method comes down to suppose the spatial domain of video and time-domain information is separate, has promptly isolated the contact between spatial domain and the time domain in testing process artificially.

In sum, at present mainly there is following feature at the present Research of the method for automatically evaluating of video quality:

One, because not deep enough to human visual system's cognitive mechanism understanding, the subjective similitude of video quality automatic evaluation method all can't reach gratifying degree at present.Therefore, current evaluation and test technology also can't reach unified, reliably, normalization period accurately.

Its two, the main impetus of evaluation and test technical research of video quality is still at the quality evaluation of still frame, and is also more elementary to the evaluation of motion sequence.Be embodied in the temporal signatures of ignoring video and isolate these two main study limitations of contact between the Space-Time domain information of video.

Its three, the center of gravity of holistic approach with the master that is applied as of " towards information source ", be that prerequisite is supposed with " the information source encoding and decoding are main deterioration root " promptly still.In fact, the deterioration of wireless video source not only is a Source Codec, and main source still is capricious wireless channel transmission.Therefore, the video quality automatic evaluation of using at wireless transmission is studied more rare.

Summary of the invention

The objective of the invention is that (that is: the subjective similitude of evaluation result is relatively poor at existing above-mentioned 3 major defects of existing video quality evaluation and test technology, wireless evaluation and test is used the supportive relatively poor of (off-line/online) and is ignored or isolate the contact of video Space-Time domain information) provide a kind of under the prerequisite that guarantees the evaluation and test performance, reduce the required reference data of evaluation and test and reduce computation complexity, make the evaluation and test model be fit to video quality automatic evaluation system and the evaluating method of using towards wireless evaluation and test thereof towards wireless network.

The evaluating method of the video quality towards wireless network of the present invention may further comprise the steps:

1) reference video (or claiming original video) of not carrying out video compression is carried out the content pre-detection earlier, the content pre-detection is to analyze the time domain activity and the spatial domain activity of reference video, adopt the activity partitioning algorithm to construct the activity subregion sequential of reference video, and according to the activity subregion sequential of reference video with reference video and video to be measured be divided into the 1st behaviour area, the 2nd behaviour area ... the N behaviour area, and the mean activity magnitude relationship of each behaviour area internal reference video is: the 1st behaviour area＞the 2nd behaviour area＞...＞the N behaviour area;

2) on above-mentioned pre-detection basis, according to each behaviour area (as the 1st behaviour area, the detection of the big young pathbreaker's video features of mean activity the 2nd behaviour area etc.) is divided into high behaviour area feature detection and low behaviour area feature detection, and acquisition can reflect the spatial domain of video-frequency band characteristics in each behaviour area and the detected parameters of temporal signatures;

3) be weighted according to the spatial domain of each behaviour area of the big young pathbreaker of mean activity of each behaviour area and the detected parameters of temporal signatures, make up the spatial domain and the temporal signatures parameter of whole section video, and, realize towards the automatic evaluation and test of the video quality of wireless network according to the video quality subjective assessment database realization detected parameters of prior structure and the mapping between the subjective quality impression.

In step 1), described time domain activity adopts following formula definition:

hod(i)＝imhist(|fn-fm|)

$\mathrm{HoD}(\mathrm{fn},\mathrm{fm})=\frac{\underset{i=0}{\overset{255}{\mathrm{\Σ}}}i*\mathrm{hod}\left(i\right)}{N_{\mathrm{pix}}}$

Wherein, f _nAnd f _mRepresent that two frames ask the image of difference, the histogram behind hod (i) expression two frame differences, N _PixRepresent the pixel number that a frame comprises, the time domain activity of the big more expression material of HOD value is high more.

In step 1), described spatial domain activity adopts gradient method to measure, and formula definition is:

$\mathrm{IAM}=\frac{1}{M*N}\left[\underset{i=1}{\overset{M-1}{\mathrm{\Σ}}}\underset{j=1}{\overset{N}{\mathrm{\Σ}}}\right|I(i,j)-I(i+1,j)|+\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\underset{j=1}{\overset{N-1}{\mathrm{\Σ}}}|I(i,j)-I(i,j+1)\left|\right]$

Wherein, the form of M and N presentation video, the I presentation video, the spatial domain activity of the big more expression material of IAM value is high more.Best N 〉=2.

In step 1), described activity partitioning algorithm is according to the comentropy maximization principle, and when video time domain activity violent (the time domain activity is bigger), the distribution of sampled point will be compared comparatively dense; And in motion change slowly when (the time domain activity is less), the distribution of sampled point will be more sparse (to add up the more movable information of multiframe, the general comentropy maximization that distributes such as reaching requires), the activity subregion sequential that adopts the activity partitioning algorithm to construct reference video may further comprise the steps:

(1) gross energy of calculating time domain activity HoD is designated as AllEg=sum (HoD), and it is carried out normalization, is expressed as HoD=HoD/ALLEg;

(2) find the big moving point of i on the HoD (i=1,2 ..., M), the unified MaxVAM that is labeled as; With MaxVAM is the center, decides window width K roughly, and promptly interval (MaxVAM-K, MaxVAM+K), the local energy in the computation interval is designated as $\mathrm{SubEg}={\∫}_{\mathrm{MaxVAM}-K}^{\mathrm{MaxVAM}+K}\mathrm{HoD}\left(x\right);$

(3) adjust the sliding window size, make SubEg equal the 1/K of gross energy (K=1,2,3 ... N), can determine the interval range of maximum active;

(4) repeating step (1)～step (3) process, determine the 2nd, 3 successively ... the big behaviour area of M scope.

In step 2) in, described high behaviour area feature detection may further comprise the steps:

(1) extract k behaviour area data of reference video and video to be measured, be designated as SrcData (i, j, k), and HrcData (k), k is 1,2 for i, j ... N;

(2) utilize edge detection operator (spatial domain edge detection operators such as Sobel operator or roberts operator) to extract the marginal zone data of reference video and video to be measured, the note boundary operator is Src _Edge(i, j, k), Hrc _Edge(i, j k), are expressed as follows:

SrcData _edge(i，j，k)＝SrcData(i，j，k)*Src _edge(i，j，k)

HrcData _edge(i，j，k)＝HrcData(i，j，k)*Hrc _edge(i，j，k)

(3) definition detects operator, realizes the tolerance to the marginal zone data in the step (2), is defined as follows:

diffROI(i，j，k)＝SrcData _edge(i，j，k)-HrcData _edge(i，j，k

${\mathrm{MSE}}_{\mathrm{ROI}}\left(k\right)=\frac{1}{Q\left(k\right)}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\underset{j=1}{\overset{N}{\mathrm{\Σ}}}\left\{{\left\{\mathrm{diffROI}(i,j,k)\right\}}^2\right\}$

${\mathrm{SI}\_\mathrm{SNR}}_{\mathrm{ROI}}\left(k\right)=10*\log \frac{{\left(\mathrm{Pixel}\right)}_{\max }^2}{{\mathrm{MSE}}_{\mathrm{ROI}}\left(k\right)}$

${\mathrm{SI}\_\mathrm{IQM}}_{\mathrm{ROI}}\left(k\right)=\frac{1}{1+\exp \{0.1701*[{\mathrm{SI}\_\mathrm{SNR}}_{\mathrm{ROI}}\left(k\right)-22.6675\left]\right\}}$

Wherein Q (k) represents total pixel value of the marginal zone data of reference video and video to be measured, wherein (Pixel) _MaxExpression picture max pixel value realizes high behaviour area feature detection.

In step 2) in, described low behaviour area feature detection may further comprise the steps:

(1) extract the k frame picture data of reference video and video to be measured, be designated as SrcData (i, j, k), and HrcData (i, j, k);

(2) the flat region masterplate of extraction reference video spatial information (si), write down as follows:

ImgNVF(i，j，k)＝NVF[SrcData(i，j，k)]

$\mathrm{ImgFlatMap}(i,j,k)=\left\{\begin{array}{cc}1,\mathrm{where}& \mathrm{ImgNVF}(i,j,k)\&GreaterEqual;T\\ 0,\mathrm{where}& \mathrm{ImgNVF}(i,j,k)<T\end{array}\right.$

Wherein T is the segmentation threshold value of flat region;

(3) extract reference video and deterioration video, write down as follows:

SrcEdgeImg(i，j，k)＝SrcData(i，j，k)*SrcEdge(i，j，k)

HrcEdgeImg(i，j，k)＝HrcData(i，j，k)*HrcEdge(i，j，k)

SrcFlatImg(i，j，k)＝ImgFlatMap(i，j，k)*SrcData(i，j，k)

HrcFlatImg(i，j，k)＝ImgFlatMap(i，j，k)*HrcData(i，j，k)

(4) detect operator according to above-mentioned definition, the detection operator of edge calculation district and flat region remembers that FlatIQM represents the spatial domain testing result at the design of flat region respectively; EdgeIQM represents the spatial domain testing result at the marginal zone design, λ ₁+ λ ₂=1, λ ₂＞λ ₁＞0,

SI_IQM(k)＝λ ₁*EdgeIQM _ROI(k)+λ ₂*FlatIQM _ROI(k)

Realize low behaviour area feature detection.

In step 3), the spatial domain of whole section video of described structure and temporal signatures parameter are that Space-Time territory detected parameters is extracted the main quality influence parameter of 6 classes, and the subitem by 6 class parameters and comprehensive, embody video quality to be measured, the spatial domain and the temporal signatures parameter of whole section video of described structure comprise following concrete steps:

(1) the defined feature parameters C 1, and in order to the deviation of the average time domain energy of standard video, characteristic parameter C1 is defined as:

$\mathrm{AvgLR}\left[\mathrm{TI}\right]=1-\frac{|\mathrm{SrcAvgTI}-\mathrm{HrcAvgTI}|}{\mathrm{SrcAvgTI}}$

If the average time domain activity of reference video is SrcAvgTI, the average time domain activity of video to be measured is HrcAvgTI.Average time domain activity is then utilized following formula:

hod(i)＝imhist(|fn-fm|)

$\mathrm{HoD}(\mathrm{fn},\mathrm{fm})=\frac{\underset{i=0}{\overset{255}{\mathrm{\&Sigma;}}}i*\mathrm{hod}\left(i\right)}{N_{\mathrm{pix}}}$

Calculate.AvgLR[TI] minimum value be 0 (being HrcAvgTI=0); Maximum is 1 (being HrcAvgTI=SrcAvgTI), AvgLR[TI] the defined parameters scope be [0-1], wherein 0 the expression performance the poorest, 1 the expression performance best.

(2) the defined feature parameters C 2, and in order to standard video frame losing incidence, characteristic parameter C2 is defined as:

$\mathrm{LR}\left[\mathrm{TI}\right]=1-\frac{\mathrm{Num}\_\mathrm{of}\_\mathrm{ZeroTi}}{\mathrm{Len}\_\mathrm{of}\_\mathrm{Section}}$

Wherein Len_of_Section is the detection segment length in the 1st behaviour area～N behaviour area, and detected video time domain activity to be measured is that zero frame number is Num_of_ZeroTi, normalized LR[TI] approximate more 1 performance of value is good more.

(3) the defined feature parameters C 3, and in order to the standard ratio of long continuous frame losing, characteristic parameter C3 is defined as:

$\mathrm{MaxLR}\left[\mathrm{TI}\right]=\frac{\mathrm{\&lambda;}}{{\mathrm{\&xi;}}^2+{(\mathrm{TI}\_\mathrm{Err}\_\mathrm{MaxLen})}^2}$

Wherein ξ and λ are positive integer, and TI_Err_MaxLen represents the longest detected lasting frame losing number, normalized MaxLR[TI] value approximate more 1 performance good more.

(4) the defined feature parameters C 4, and characteristic parameter C4 is:

$\mathrm{AvgLR}\left[\mathrm{SI}\right]=1-\frac{|\mathrm{MeanSrcIAM}-\mathrm{MeanHrcIAM}|}{\mathrm{MeanSrcIAM}}$

Wherein MeanSrcIAM and MeanHrcIAM are respectively the mean value of the spatial domain activity of the one section video that calculates.AvgLR[SI] minimum value be 0; Maximum is 1, and wherein 0 expression performance is the poorest, and 1 expression performance is best.

(5) the defined feature parameters C 5, are used to express estimate divide the lasting cycle that is lower than a certain tolerance value, and characteristic parameter C5 is:

$\mathrm{MaxLR}\left[\mathrm{SI}\right]=\frac{\mathrm{\&lambda;}}{{\mathrm{\&xi;}}^2+{(\mathrm{SI}\_\mathrm{Err}\_\mathrm{MaxLen})}^2}$

Wherein SI_Err_MaxLen represents to be lower than the lasting Cycle Length that expectation divides (0～1), and its value is distributed between (0,1), and wherein approximate more 1 performance is good more.

(6) the defined feature parameters C 6, are used to express comprehensive spatial domain quality branch, and to using weighted comprehensive between the different behaviour areas, and the spatial domain that finally obtains whole section video to be measured detects index, and characteristic parameter C6 is:

IQM[SI]＝ξ ₁*SI_IQM _High+ξ ₂*SI_IQM _Low

Wherein: ξ ₁+ ξ ₂=1, SI_IQM _HighAnd SI_IQM _LowRepresent the high behaviour area feature detection in the step (2) and the result of low behaviour area feature detection respectively.

(7) 6 characteristic parameters of step (1)～step (6) are carried out last comprehensive again, can obtain the comprehensive quality of the video that detects.Combined process is divided into 3 weights influence priority with 6 category feature parameters, and promptly C3 and C5 have the highest grade that influences; C2 and C6 have the grade that influences placed in the middle; And C1 and C4 to influence grade relative minimum, quality divides the pass of satisfying to be:

VideoScore＝k1*(C1、C4)+k2*(C2、C6)+k3*(C3、C5)

Wherein (Ci, Cj) expression influences grade, and ki represents to wait the weighted value of inter-stage, wherein k1＜k2＜k3, and k1+k2+k3=1.

The automatic evaluation system of the video quality towards wireless network of the present invention is provided with that information source is obtained and code device, deterioration emulation and analogue means and video quality automatic evaluation device.

Information source deriving means: be used for the picture signal of acquisition camera picked-up and import existing video file.The information source deriving means is provided with camera, memory and real-time process chip (MCU), and camera output connects the input port of real-time process chip, and memory links to each other with real-time process chip, and lead-in mode drives camera by MCU and intercepts video image input in real time; File imports then and by PC download line video file is imported on this evaluation and test platform.

Deterioration emulation and analogue means: the emulation and the simulation of the video degradation form that is used to provide common obtain typical degradation feature in the wireless video transmission by emulation and simulation.Deterioration emulation and analogue means are made up of real-time process chip and peripheral storage, and peripheral storage solidifies the degradation treatment model in advance, produce the deterioration form by transaction module, and peripheral storage is connected with real-time process chip.

The video quality automatic evaluation device: the video quality automatic evaluation device is provided with process chip, memory and peripheral auxiliary equipment, and memory connects with process chip, and the computer expert crosses the download line and connects with process chip.

The present invention includes video quality evaluation and test function at off-line application and online application.

Real-time lead-in mode in the information source deriving means drives the input of camera intercepting video image by MCU, supports the video format of 320 * 240,176 * 144,352 * 288 3 kinds of resolution that wireless video is commonly used at present.

Chip in the real-time process chip available sources deriving means of deterioration emulation and analogue means.Transaction module in deterioration emulation and the analogue means produce the deterioration form specifically comprise fuzzy pictures simulation, picture add the simulation of making an uproar, frame solidification simulation, frame-skip simulation, based on the wireless video Packet Loss Simulation of message switching scheme, simulate based on the wireless video error code emulation of circuit exchange mode and based on the point-to-point transmission of wireless network card 802.11b agreement.

The peripheral auxiliary equipment of video quality automatic evaluation device comprises that download line, power supply etc. can be used for the annex in the information source deriving means.

The present invention has overcome existing above-mentioned 3 major defects of existing video quality evaluation and test technology, and (that is: the subjective similitude of evaluation result is relatively poor, wireless evaluation and test is used the supportive relatively poor of (off-line/online) and is ignored or isolate the contact of video Space-Time domain information) provide a kind of under the prerequisite that guarantees the evaluation and test performance, reduce the required reference data of evaluation and test and reduce computation complexity, make the evaluation and test model be fit to video quality automatic evaluation system and the evaluating method of using towards wireless evaluation and test thereof towards wireless network.

Embodiment

Following examples will the present invention is further illustrated.

At first, the input (the download line of PC) by the information source deriving means imports this evaluating system with reference video file (being designated as SRC); Secondly, this reference video through deterioration emulation and analogue means, is obtained required video degradation file (being designated as HRC); On the bases in above-mentioned two steps, then SRC and HRC can be sent into and carry out quality assessment in the video quality automatic evaluation device.

Below further specify the concrete technical application of the automatic evaluating apparatus of video quality.

(1) content pre-detection

Calculate the time domain activity (being designated as SrcTi) of SRC by the time domain detection of activity formula in the summary of the invention, and the time domain activity is carried out subregion according to the information entropy principle.As the time domain activity distribution character (preceding 100 frames) of Suzie sequence, the piecemeal activity that gets rid of head at Suzie is higher, and other regional activity are then relatively low.The number of activity subregion can be adjusted according to actual, and particularly, the subregion number of native system acquiescence is 2 (high behaviour area and low behaviour areas).Adopt above-mentioned activity partitioning strategies, the size and the activity of windowing is inversely proportional to (be activity is greatly then windowed little, vice versa), thereby can reasonably adjust the specification of subregion automatically according to content.

(2) temporal signatures detects

The major influence factors of time domain deterioration is the duration of deterioration and the frequency of generation.The duration also often has in the television relay once in a while very brief picture solidify in the life and jumps, as long as can't be caused too big influence to subjective feeling in so-called " tolerance " scope.Therefore, the quality influence degree of time domain deterioration is restricted by three factors mainly, that is: the size of difference degree is designated as TI_Error_Extent; The length of duration is designated as TI_Error_Len; And the frequency of phenomenon generation, be designated as TI_Error_Freq.If by goal activities subregion (high behaviour area or low behaviour area) the internal reference video of structure sequential intercepting and the time domain activity of video to be measured is respectively SrcObjTi and HrcObjTi, the energy difference that then detects time domain is diffTi, the polarity of different diffTi is expressed different deterioration features, and physical relationship is as described below:

DiffTI be on the occasion of, the time domain energy of representing material to be measured is lower than the time domain energy with reference to material, illustrates that time domain energy having occurred loses phenomenon; When diffTI is a negative value, the time domain energy of representing material to be measured is higher than the time domain energy with reference to material, illustrates that time domain energy having occurred increases phenomenon; And be approximate null value as diffTI, and represent that then energy is suitable between the two, represent no deterioration or have deterioration but offset.Situation to diffTi=0 is not discussed (the ordinary representation time domain there is not damage, and deterioration existence and the probability of happening that offsets are lower).DiffTi non-zero situation is then needed further to carry out refinement to be detected.

The parameter expression of temporal signatures and description are referring to table 1.

Table 1

From the following as can be known important enlightenment of above-mentioned analysis: 1. slightly losing of time domain energy can cause diffTi by a small margin greater than zero; And time domain energy lose significantly (except that diffTi＞0, HrcObjTi ≈ 0) then point out may occur such as frame solidify, serious deterioration form such as frame-skip.2. above-mentioned deterioration depends on also that to the size that influences of subjective quality HrcObjTi continues to be lower than the time length of certain thresholding (promptly approach zero or equal zero).3. in addition, all diffTi＞0 can appear though frame solidifies with frame-skip, the phenomenon of HrcObjTi ≈ 0, and frame is set in the pulse that does not have diffTi＜0 after the above-mentioned phenomenon.Therefore, whether occurring the time domain energy increase after the frame losing can be used as and judge whether system recovers one of sign of normal play.4. the amplitude that occurs shock pulse thereafter can be used as one of auxiliary foundation of the subjective impact effect of judging this deterioration.

(3) spatial feature detects

The principal element that influences wireless video spatial domain quality is: the intensity of deterioration is designated as SI_Error_Extent; The position of deterioration is designated as SI_Error_Location; And the time domain masking property of deterioration, be designated as SI_Error_TimeMask.Relevant feature description and analyze as shown in table 2.

Table 2

Influencing factor	Signature analysis and explanation
		Intensity (SI_Error_Extent)	Show as difference with surrounding pixel.When the difference with surrounding pixel surpasses certain threshold value, point out stronger spatial domain deterioration to have occurred.Essence is one of index of spatial concealment.
Position, spatial domain (SI_Error_Location)	Show as the position, spatial domain of appearance.The deterioration of same degree the position occurs in different spatial domains and often causes different visual sensitivities, thereby influences subjective quality.
		Time domain is covered (SI_Error_TimeMask)	Be expressed as the activity size of the frame of video of deterioration appearance.If in higher time domain behaviour area, can tolerate more spatial domain deterioration distortion usually.

Above-mentioned 3 influencing factors have defined the character of spatial feature from different point of theory.1. the SI_Error_TimeMask time domain that embodied video is covered the relation with the time domain activity, promptly before and after frame difference when big, human eye HVS will tolerate spatial domain deterioration largely.Therefore the deterioration of position appears in equality strength and spatial domain, if appear at lower time domain behaviour area, will be easier to be more perceived than appearing at higher time domain behaviour area, and cause the reduction of corresponding perceived quality.2. SI_Error_Location embodies the spatial concealment of video and the relation of spatial domain activity, and when promptly details is enriched (being that the spatial domain activity is bigger), human eye HVS will tolerate spatial domain deterioration largely.Therefore the deterioration of equality strength, if appear at lower behaviour area, spatial domain, will be easier to be more perceived than appearing at behaviour area, higher spatial domain, thus cause the reduction of corresponding perceived quality.3. SI_Error_Extent has then embodied the relation of spatial concealment and brightness, contrast, that is: the background luminance of deterioration place pixel is crossed brightly or dark excessively, and corresponding contrast is less, all can make the perceptibility of deterioration reduce.It is one of the highest factor of weighing factor that obvious time domain is covered characteristic.The power that time domain is covered depends on the size of time domain activity, therefore can instruct the detection resolution in spatial domain by temporal signatures.Particularly be to adopt lower spatial resolution in high time domain behaviour area; Adopt higher spatial resolution in low time domain behaviour area.Therefore, the design of spatial feature parameter is divided into the spatial feature design of high behaviour area according to different behaviour areas and hangs down the spatial feature design of behaviour area.

(A) spatial feature of high behaviour area detects

The zone that the time domain activity is higher, the material conversion is very fast, the response time is nervous, and HVS reduces the resolution capability of spatial domain details, is fit to therefore adopt that operand is less, response time detection algorithm faster.Detect by spatial domain than coarse resolution, but system overhead conserved, and rule is answered in the Space-Time frequency response that meets HVS; Simultaneously, use bigger time-domain sampling rate (frame by frame), with the difference of real-time capturing visual.In addition, studies show that in higher time domain behaviour area, the spatial resolving power of HVS often only is confined to the extraction to the picture profile information.Therefore, in the treatment mechanism of high time domain behaviour area, the emphasis that the spatial domain is detected will be confined to the detection to the contour edge deterioration.

The deterioration distortion situation that should pay close attention to the edge contour district is detected in the spatial domain of high time domain behaviour area.The variation of image pixel intensity has formed the profile of image, has reflected the content of image.And image gradient has exactly reflected the variation of image-region pixel intensity.Image gradient has reflected the size and Orientation that the regional area pixel value changes.The size of gradient has reflected the speed that image pixel changes.Grad at the place, image border is bigger usually, and is then less at the Grad of image flat region.Gradient image has been given prominence to the feature and the marginal information of image.The edge of image profile has determined the content of image, has reflected the space structure of image.The distortion of image outline has also reflected the distortion level of picture material well, thereby influences the subjective quality of picture.(x, y) gradient fields is through the preliminary treatment image f of (comprising brightness rectification and gaussian filtering) in definition

G wherein _x(x, y), g _y(x y) represents gradient respectively In the horizontal direction with the component of vertical direction.

$\stackrel{\&RightArrow;}{g}(x,y)=g_x(x,y)\stackrel{\&RightArrow;}{i}+g_y(x,y)\stackrel{\&RightArrow;}{j}$

$g_x(x,y)=\frac{\&PartialD;f(x,y)}{\&PartialD;x}$

$g_y(x,y)=\frac{\&PartialD;f(x,y)}{\&PartialD;y}$

Thereby can obtain gradient

Size

And gradient

Direction

Be expressed as follows:

$|\stackrel{\&RightArrow;}{g}(x,y)|=\sqrt{g_x{(x,y)}^2+g_y{(x,y)}^2}$

If through pretreated with reference to material be SrcData (i, j, k), i wherein, j represents the spatial domain coordinate, k represents sequential; Material to be measured is that (i, j k), are Src with reference to the edge detection template of material to HrcData _Edge(k), the edge detection template of material to be measured is Hrc for i, j _Edge(i, j k), then have:

SrcData _edge(i，j，k)＝SrcData(i，j，k)*Src _edge(i，j，k)

HrcData _edge(i，j，k)＝HrcData(i，j，k)*Hrc _edge(i，j，k)

PreData _edge(i，j，k)＝HrcData(i，j，k)*Src _edge(i，j，k)

Wherein, SrcData _Edge(k) expression is with reference to the edge pixel information of material for i, j; HrcData _Edge(i, j, k) the edge pixel information of expression material to be measured; PreData _Edge(k) expression is with reference to the material information to be measured of material mapping for i, j.PreData wherein _EdgeMarginal information loss to the reference material is responsive; HrcData _EdgeThen responsive to the spatial information (si) increase.

(B) spatial feature of low behaviour area detects

The spatial domain of low time domain behaviour area is detected and should be adhered to the degrade minute analysis method of laying equal stress in marginal zone and flat region.The marginal zone detection side overweights the degradation to the picture original structure; The flat region is detected then to lay particular emphasis on and is detected the newly-increased deterioration form that picture is easily discovered the district.By the cooperation of the two, can detect two kinds of situations that influence the spatial domain quality preferably.

If through pretreated with reference to material be SrcData (i, j, k), i wherein, j represents the spatial domain coordinate, k represents sequential; Material to be measured is that (i, j k), are that (k), the edge detection template of material to be measured is that (k), definition mode is same as above for i, j for HrcEdge to SrcEdge for i, j with reference to the edge detection template of material to HrcData.Note with reference to material through the filtered material of noise visible function (NVF, Noise VisibilityFunction) be ImgNVF (i, j, k), NVF[wherein] expression uses the NVF computational methods.

ImgNVF(i，j，k)＝NVF[SrcData(i，j，k)]

(i, j have comprised the noise takeover information with reference to the material picture in k) to ImgNVF, and its value big more (being distributed between [0～1]) is then expressed this part and is in the flat region, and deterioration form at this moment will relatively easily be discovered.(i, j k) are the basis, extract the flat region index map of picture, and (k), expression formula is as follows for i, j, and wherein T is the threshold value thresholding, in order to flat region and marginal zone are separated (average that T is defined as ImgNVF) to be designated as ImgFlatMap with ImgNVF.

$\mathrm{ImgFlatMap}(i,j,k)=\left\{\begin{array}{cc}1,\mathrm{where}& \mathrm{ImgNVF}(i,j,k)\&GreaterEqual;T\\ 0,\mathrm{where}& \mathrm{ImgNVF}(i,j,k)<T\end{array}\right.$

Respectively with ImgFlatMap (i, j k) are masterplate, to reference/testing image SrcData (i, j, k) and HrcData (i, j k) carry out " mask ", thereby the material content are cut apart roughly.The pixel difference of calculating these positions changes, and can obtain the relevant detection parameter.Because the deterioration form of wireless video is square and fuzzy mostly.Wherein blurring effect causes the expansion at edge, the marginal information of material self is weakened even loses; Square then makes the material flat region the external deterioration form of easily being discovered occur.Therefore, the detected parameters of marginal zone can embody the degree of blurring effect; The detected parameters of flat region then can embody the observability of external deterioration.According to foregoing description, can obtain four partial contents: the reference material/material image information SrcFlatImg to be measured that obtains through flat region mapping (i, j, k), HrcFlatImg (i, j, k); Reference/material the content to be measured that extracts through the marginal zone mapping is that (k), (k), the correlated expression formula is as follows for i, j for HrcEdgeImg for i, j for SrcEdgeImg.

SrcEdgeImg(i，j，k)＝SrcData(i，j，k)*SrcEdge(i，j，k)

HrcEdgeImg(i，j，k)＝HrcData(i，j，k)*HrcEdge(i，j，k)

SrcFlatImg(i，j，k)＝ImgFlatMap(i，j，k)*SrcData(i，j，k)

HrcFlatImg(i，j，k)＝ImgFlatMap(i，j，k)*HrcData(i，j，k)

(C) spatial feature parametrization

(A) be to obtain different behaviour areas in the difference of paying close attention on the content with the purpose of design of (B).High behaviour area time domain masking property is bigger, so focal point is to analyze deterioration and whether appears at the picture edge; Low behaviour area time domain masking property a little less than, therefore the emphasis of paying close attention to also comprises the flat region of easily discovering deterioration except the picture edge.Therefore, purpose of design (A) and (B) is to obtain pairing region-of-interest data.On this basis, the parametrization of spatial feature is expressed as follows described, wherein total pixel value of Q (k) expression interest figure.Wherein (Pixel) max represents picture max pixel value, SI_SNR _ROI(k) the spatial domain detected parameters (db unit) in expression k frame activity district (high or low), SI_IQM _ROI(k) the image quality parameter (numerical value is between 0～1) in expression k frame activity district (high or low).

diffROI(i，j，k)＝SrcImg(i，j，k)-HrcImg(i，j，k)

${\mathrm{MSE}}_{\mathrm{ROI}}\left(k\right)=\frac{1}{Q\left(k\right)}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}\left\{{\left\{\mathrm{diffROI}(i,j,k)\right\}}^2\right\}$

${\mathrm{SI}\_\mathrm{SNR}}_{\mathrm{ROI}}\left(k\right)=10*\log \frac{{\left(\mathrm{Pixel}\right)}_{\max }^2}{{\mathrm{MSE}}_{\mathrm{ROI}}\left(k\right)}$

${\mathrm{SI}\_\mathrm{IQM}}_{\mathrm{ROI}}\left(k\right)=\frac{1}{1+\exp \{0.1701*[{\mathrm{SI}\_\mathrm{SNR}}_{\mathrm{ROI}}\left(k\right)-22.6675\left]\right\}}$

For ease of distinguishing, note FlatIQM represents the spatial domain testing result at the design of flat region; EdgeIQM represents the spatial domain testing result at the marginal zone design.For high behaviour area, λ ₁=1, λ ₂=0; For low behaviour area, following formula λ ₁+ λ ₂=1, λ ₂＞λ ₁＞0.

SI_IQM(k)＝λ ₁*EdgeIQMROI(k)+λ ₂*FlatIQMROI(k)

To using weighted comprehensive between the different behaviour areas, and the spatial domain that finally obtains whole section video to be measured detects index, is expressed as follows, wherein: ξ ₁+ ξ ₂=1, weighted value can be based on concrete application training adjustment.ROI is expressed as the attribute of the highest behaviour area (High) or smooth behaviour area (Low), ${\mathrm{SI}\_\mathrm{IQM}}_{\mathrm{ROI}}=\underset{k}{\mathrm{\&Sigma;}}\mathrm{SI}\_\mathrm{IQM}\left(k\right).$

IQM[SI]＝ξ ₁*SI_IQM _High+ξ ₂*SI_IQM _Low

(4) parametrization of feature design

Present embodiment again according to characteristics separately and influence size, extracts following 6 classifications (C1～C6) to " temporal signatures " and " spatial feature " parameter.In the table, the note reference video is Src, and video to be measured is Hrc.The effect weight that influences parameter of six classifications of C1～C6 is different, and 3 attribute weights separately are also variant.For ease of describing, according to the hierarchical structure of parameter, the configuration of evaluating is divided into 3 levels in the present embodiment, be respectively: " variable layer ", " attribute layer " and " class layer ".Evaluating classification list is referring to table 3.

Table 3

Classification	Item name	Definition and description of use
			C1	AvgLR[TI]	The time domain activity mean difference of Src and Hrc; In order to the increase and lose of tolerance time domain energy
C2	LR[TI]	The occurrence frequency of Hrc LOF phenomenon; In order to measure overall frame loss rate
			C3	MaxLR[TI]	The long period that the Hrc LOF continues; In order to measure the duration that picture solidifies
C4	AvgLR[SI]	The spatial domain activity inequality of Src and Hrc; In order to tolerance spatial domain energy increase and lose
			C5	MaxLR[SI]	Hrc spatial domain MOS divides the lasting cycle that is lower than the expectation branch; In order to measure continuous deterioration
C6	IQM[SI]	The spatial domain quality quantification value of picture is in order to the spatial domain quality branch of tolerance Hrc

The higher district of activity comprehensively has a bigger weighted value to time domain class parameter; The low district of activity comprehensively has a bigger weighted value to spatial domain class parameter.Though C1～C6 class evaluation of estimate has been expressed the mass property of video from 6 angles, the weighing factor that these 6 angles are divided final single quality there are differences.Therefore, the purpose of class equipping rules is that 6 class evaluations of estimate are further carried out comprehensively again, to obtain single evaluation result.

In video quality evaluation, fluency and definition are two important index.Video quality evaluation has been different from more than at all just being of rest image evaluation the concern to the fluency index.Wherein fluency is mainly embodied by time domain parameter; Definition is then mainly embodied by the spatial domain parameter.In addition, subjective test shows, the final mass of a certain video to be measured can be subjected to wherein certain several the poorest section influence usually, and promptly the poorest section owing to give deep impression easily, therefore has bigger quality influence weighted value.In addition, people can have tangible detest sense to the low deterioration that continue to surpass certain hour, if promptly time of continuing of certain serious deterioration be no more than certain thresholding, therefore people can't give and lower score.Comprehensive above-mentioned subjective characteristic has been set Three Estate to class is synthetic, and promptly C3 and C5 have the highest grade that influences; C2 and C6 have the grade that influences placed in the middle; And C1 and C4 to influence grade relative minimum.Be that final quality branch satisfies following relation, wherein (Ci, Cj) expression influences grade, and ki represents to wait the weighted value of inter-stage, wherein k1＜k2＜k3, and k1+k2+k3=1.

VideoScore＝k1*(C1、C4)+k2*(C2、C6)+k3*(C3、C5)

(A) parametrization of C1 category feature

AvgLR[TI] defined the average departure degree on the time domain activity with reference to material and material to be measured.Usually hour, the average departure of the two also less (even coincidence) in time domain energy loss; When the time domain energy loss is big, the average time domain activity of its material to be measured will be lower than with reference to material.Be the characterising parameter process, the average time domain energy of establishing with reference to material is SrcAvgTI, and the average time domain energy of material to be measured is HrcAvgTI.What These parameters reflected is interior total time domain active characteristics of video a period of time.Defined parameters AvgLR[TI], in order to express the mean deviation of time domain activity, be expressed as follows:

$\mathrm{AvgLR}\left[\mathrm{TI}\right]=1-\frac{|\mathrm{SrcAvgTI}-\mathrm{HrcAvgTI}|}{\mathrm{SrcAvgTI}}$

The time domain average energy of material to be measured is not more than with reference to material on the statistics aspect usually, and time domain energy is non-negative, therefore from above-mentioned definition as can be known, AvgLR[TI] minimum value be 0 (being HrcAvgTI=0); Maximum is 1 (being HrcAvgTI=SrcAvgTI), AvgLR[TI] the defined parameters scope be [0-1], wherein 0 the expression performance the poorest, 1 the expression performance best.

(B) parametrization of C2 category feature

LR[TI] what define is that material time domain energy to be measured is the probability of happening of zero (or being lower than thresholding) situation.Because time domain energy is zero to usually occur under the LOF situation, so this index describes is to be measured section the total occurrence rate of LOF.Need to prove that the denominator of the ratio here is meant one of the highest time domain behaviour area, inferior high time domain behaviour area, three attributes in smooth time domain behaviour area, but not full material.If certain detects segment length be Len_of_Section (the activity subregion sequential that can obtain by pre-detection is its value quantitatively), wherein detected material time domain energy to be measured is the zero Num_of_ZeroTi that adds up to, then C2 class evaluating redefine into:

$\mathrm{LR}\left[\mathrm{RI}\right]=1-\frac{\mathrm{Num}\_\mathrm{of}\_\mathrm{ZeroTi}}{\mathrm{Len}\_\mathrm{of}\_\mathrm{Section}}$

Because the Num_of_ZeroTi scope be [0～Len_of_Section], so LR[TI] value be distributed between [0～1], wherein 0 represents to be measured section performance the poorest (being lost frames all); Performance is best (lost frames do not occur, Num_of_ZeroTi=0) in 1 expression.

(C) parametrization of C3 category feature

MaxLR[TI] what define is that material time domain energy to be measured is continuously zero the longest quality influence, it is further replenishing of above-mentioned C2 class.Be that the content that the C2 class detects may comprise two kinds of situations: frame solidifies and frame-skip, is that the picture of above-mentioned two situations solidifies length and the C3 class detects.Maximum 300 frames of length of evaluation and test material of ITU-T definition, each detection segment (the highest behaviour area, inferior high behaviour area, flat region) is also variable to the length that frame solidifies, and the dynamic range of C3 class parameter is bigger, can not be directly in order to quality evaluation.Adopt the bell function that data are done process of fitting treatment, the definition expression formula is as follows:

$\mathrm{MaxLR}\left[\mathrm{TI}\right]=\frac{\mathrm{\&lambda;}}{{\mathrm{\&xi;}}^2+{(\mathrm{TI}\_\mathrm{Err}\_\mathrm{MaxLen})}^2}$

ξ=30 wherein, λ=900, TI_Err_MaxLen represents the longest detected lasting frame losing number.Normalized MaxLR[TI] value approximate more 1 performance good more.

(D) parametrization of C4 category feature

AvgLR[SI] what define is the record index of the variation (being IAM) of spatial domain activity, the variation of IAM has embodied material spatial domain deterioration feature.Usually hour, the average departure of SrcIAM and HrcIAM also less (even coincidence) in the spatial domain deterioration; When energy loss is big, changing will appear in its average energy average.IAM relation between analysis SRC and the HRC between 1～2 frame is too big meaning not, and feasible method is that its statistic is analyzed.As establish certain the section (the highest behaviour area, inferior high behaviour area or smooth behaviour area) reference video the spatial domain activity be SrcIAM (k), k=1,2 ... M; The spatial domain activity of corresponding section video to be measured is HrcIAM (k), k=1, and 2 ... M.Then can analyze its average MeanSrcIAM and MeanHrcIAM, be defined as follows:

$\mathrm{MeanSrcIAM}=\frac{1}{M}\underset{k=1}{\overset{M}{\mathrm{\&Sigma;}}}\mathrm{SrcIAM}\left(k\right)$

$\mathrm{MeanHrcIAM}=\frac{1}{M}\underset{k=1}{\overset{M}{\mathrm{\&Sigma;}}}\mathrm{HrcIAM}\left(k\right)$

Then the difference of the two will embody above-mentioned three kinds of relations, i.e. diffIAM=MeanSrcIAM-MeanHrcIAM.Difference is good more near 0 quality more, and it is big more that difference departs from 0 expression damage.With diffIAM＜0 is analysis, and it is mainly derived from encoder compresses (probability is bigger) and the fuzzy deterioration form (probability is less) when longer.For ease of follow-up evaluation, definition C4 evaluating is as follows:

$\mathrm{AvgLR}\left[\mathrm{SI}\right]=1-\frac{|\mathrm{MeanSrcIAM}-\mathrm{MeanHrcIAM}|}{\mathrm{MeanSrcIAM}}$

When MeanSrcIAM equals MeanHrcIAM, AvgLR[SI]=not 1 (expression does not have loss substantially); When MeanSrcIAM and MeanHrcIAM difference are excessive, then can set to block make AvgLR[SI]=0.

(E) parametrization of C5 category feature

MaxLR[SI] the evaluation branch that is defined as the C5 class is lower than the lasting cycle of a certain tolerance value (peripheral hardware according to demand), and its essence is further replenishing of detecting of C4 and C6 class.The content that the C5 class detects is that the poorest of spatial domain distortion may situation.(annotate: the cycle of the taking a sample test difference of different attribute, therefore need be converted to frame number unit) is decided to be more than the thd_Len=90 frame and is difficult to accept (adjustable) to continue the cycle.Expression formula is as follows behind the definition C5.Wherein SI_Err_MaxLen represents to be lower than the lasting Cycle Length that certain expectation divides.Its value is distributed between (0,1), and wherein approximate more 1 performance is good more.

$\mathrm{MaxLR}\left[\mathrm{SI}\right]=\frac{\mathrm{\&lambda;}}{{\mathrm{\&xi;}}^2+{(\mathrm{SI}\_\mathrm{Err}\_\mathrm{MaxLen})}^2}$

(F) parametrization of C6 category feature

IQM[SI] expression video to be measured overall spatial domain quality branch.It detects definition shown in (3).

(G) quality of C1～C6 class parameter is expressed

C1～C6 six class indexs of the present invention definition are all expressed the degree of the some aspects of this video, the quality that can measure one section video all sidedly by these six indexs.6 quantizating index of C1～C6 are corresponded to orthohexagonal six summits respectively, and this polygons is represented the expectation index of (performance the best, the most complete) under the desirable sight.The testing result of each index is mapped to respectively on its corresponding reference axis, and its length has been expressed the relation that detects index and ideal value, the tolerance of the actual detection performance that obtains of its expression, and then performance is good more the closer to peripheral summit on the inner polygon summit.In other words, quality is unreasonable if the results for video that detects makes that net figure Zhang Deyue opens thinks.(RMSE, PCC), all be better than the PSNR method on monotonicity (SCC) and this four classes index of coincident indicator (OR), the contrast relationship of the two is as shown in table 4 in the forecasting accuracy of algorithm in the present invention.

Table 4

Category of test/algorithm	PSNR	SV_IQM
			Pearson Corr	0.67963	0.85388
Spearman Corr	0.71047	0.82403
			RootMSE	0.171047	0.10315
Outlier Ratio	0.60	0.275

(5) the subjective assessment database makes up and mapping

The structure of subjective assessment database mainly makes up mapping relations between subjective assessment and the test parameter according to subjective assessment standards such as BT.500-8 to training video in advance.In order to make database can contain main types of degradation as far as possible, except the video of collecting representative content (comprising different skies/time domain activity characteristics), each video file has been designed deterioration form dissimilar and in various degree, the subjective assessment database of training material be can make up by the way preferably, and the detected parameters of other materials and the mapping between people's subjective feeling realized with this.

Claims (10)

1. towards the evaluating method of the video quality of wireless network, it is characterized in that may further comprise the steps:

1) reference video of not carrying out video compression is carried out the content pre-detection earlier, the content pre-detection is to analyze the time domain activity and the spatial domain activity of reference video, adopt the activity partitioning algorithm to construct the activity subregion sequential of reference video, and according to the activity subregion sequential of reference video with reference video and video to be measured be divided into the 1st behaviour area, the 2nd behaviour area ... the N behaviour area, and the mean activity magnitude relationship of each behaviour area internal reference video is: the 1st behaviour area＞the 2nd behaviour area＞...＞the N behaviour area;

2) on above-mentioned pre-detection basis, detection according to the big young pathbreaker's video features of mean activity of each behaviour area is divided into high behaviour area feature detection and low behaviour area feature detection, and acquisition can reflect the spatial domain of video-frequency band characteristics in each behaviour area and the detected parameters of temporal signatures;

3) be weighted according to the spatial domain of each behaviour area of the big young pathbreaker of mean activity of each behaviour area and the detected parameters of temporal signatures, make up the spatial domain and the temporal signatures parameter of whole section video, and, realize towards the automatic evaluation and test of the video quality of wireless network according to the video quality subjective assessment database realization detected parameters of prior structure and the mapping between the subjective quality impression.

2. the evaluating method of the video quality towards wireless network as claimed in claim 1 is characterized in that in step 1) described time domain activity adopts following formula definition:

hod(i)＝imhist(|fn-fm|)

Wherein, f _nAnd f _mRepresent that two frames ask the image of difference, the histogram behind hod (i) expression two frame differences, N _PixRepresent the pixel number that a frame comprises, the time domain activity of the big more expression material of HOD value is high more.

3. the evaluating method of the video quality towards wireless network as claimed in claim 1 is characterized in that in step 1), and described spatial domain activity adopts gradient method to measure, and formula definition is:

$\mathrm{IAM}=\frac{1}{M*N}\left[\underset{i=1}{\overset{M-1}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}\right|I(i,j)-I(i+1,j)|+\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{N-1}{\mathrm{\&Sigma;}}}|I(i,j)-I(i,j+1)\left|\right]$

Wherein, the form of M and N presentation video, the I presentation video, the spatial domain activity of the big more expression material of IAM value is high more.

4. the evaluating method of the video quality towards wireless network as claimed in claim 1, it is characterized in that in step 1), described activity partitioning algorithm is according to the comentropy maximization principle, more violent in the video time domain activity, be time domain activity when big, the distribution of sampled point will be compared comparatively dense; And slower in motion change, promptly the time domain activity hour, the distribution of sampled point will be more sparse, the activity subregion sequential that adopts the activity partitioning algorithm to construct reference video may further comprise the steps:

(1) gross energy of calculating time domain activity HoD is designated as AllEg=sum (HoD), and it is carried out normalization, is expressed as HoD=HoD/ALLEg;

(2) find the big moving point of i on the HoD (i=1,2 ..., M), the unified MaxVAM that is labeled as; With MaxVAM is the center, decides window width K roughly, and promptly interval (MaxVAM-K, MaxVAM+K), the local energy in the computation interval is designated as $\mathrm{SubEg}={\&Integral;}_{\mathrm{MaxVAM}-K}^{\mathrm{MaxVAM}+K}\mathrm{HoD}\left(x\right);$

(3) adjust the sliding window size, make SubEg equal the 1/K of gross energy (K=1,2,3 ... N), can determine the interval range of maximum active;

(4) repeating step (1)～step (3) process, determine the 2nd, 3 successively ... the big behaviour area of M scope.

5. the evaluating method of the video quality towards wireless network as claimed in claim 1 is characterized in that in step 2) in, described high behaviour area feature detection may further comprise the steps:

(1) extract k behaviour area data of reference video and video to be measured, be designated as SrcData (i, j, k), and HrcData (k), k is 1,2 for i, j ... N;

(2) utilize edge detection operator (spatial domain edge detection operators such as Sobel operator or roberts operator) to extract the marginal zone data of reference video and video to be measured, the note boundary operator is Src _Edge(i, j, k), Hrc _Edge(i, j k), are expressed as follows:

SrcData _edge(i，j，k)＝SrcData(i，j，k)*Src _edge(i，j，k)

HrcData _edge(i，j，k)＝HrcData(i，j，k)*Hrc _edge(i，j，k)；

(3) definition detects operator, realizes the tolerance to the marginal zone data in the step (2), is defined as follows:

diffROI(i，j，k)＝SrcData _edge(i，j，k)-HrcData _edge(i，j，k

${\mathrm{MSE}}_{\mathrm{ROI}}\left(k\right)=\frac{1}{Q\left(k\right)}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}\left\{{\left\{\mathrm{diffROI}(i,j,k)\right\}}^2\right\}$

$\mathrm{SI}\_{\mathrm{SNR}}_{\mathrm{ROI}}\left(k\right)=10*\log \frac{{\left(\mathrm{Pixel}\right)}_{\max }^2}{{\mathrm{MSE}}_{\mathrm{ROI}}\left(k\right)}$

$\mathrm{SI}\_{\mathrm{IQM}}_{\mathrm{ROI}}\left(k\right)=\frac{1}{1+\exp \{0.1701*[\mathrm{SI}\_{\mathrm{SNR}}_{\mathrm{ROI}}\left(k\right)-22.6675\left]\right\}}$

Wherein Q (k) represents total pixel value of the marginal zone data of reference video and video to be measured, wherein (Pixel) _MaxExpression picture max pixel value realizes high behaviour area feature detection.

6. the evaluating method of the video quality towards wireless network as claimed in claim 1 is characterized in that in step 2) in, described low behaviour area feature detection may further comprise the steps:

(1) extract the k frame picture data of reference video and video to be measured, be designated as SrcData (i, j, k), and HrcData (i, j, k);

(2) the flat region masterplate of extraction reference video spatial information (si), write down as follows:

ImgNVF(i，j，k)＝NVF[SrcData(i，j，k)]

$\mathrm{ImgFlatMap}(i,j,k)=\left\{\begin{array}{c}1,\mathrm{where\; ImgNVF}(i,j,k)\&GreaterEqual;T\\ 0,\mathrm{where\; ImgNVF}(i,j,k)<T\end{array}\right.$

Wherein T is the segmentation threshold value of flat region;

(3) extract reference video and deterioration video, write down as follows:

SrcEdgeImg(i，j，k)＝SrcData(i，j，k)*SrcEdge(i，j，k)

HrcEdgeImg(i，j，k)＝HrcData(i，j，k)*HrcEdge(i，j，k)

SrcFlatImg(i，j，k)＝ImgFlatMap(i，j，k)*SrcData(i，j，k)

HrcFlatImg(i，j，k)＝ImgFlatMap(i，j，k)*HrcData(i，j，k)；

(4) detect operator according to above-mentioned definition, the detection operator of edge calculation district and flat region remembers that FlatIQM represents the spatial domain testing result at the design of flat region respectively; EdgeIQM represents the spatial domain testing result at the marginal zone design, λ ₁+ λ ₂=1, λ ₂＞λ ₁＞0,

SI_IQM (k)=λ ₁* EdgeIQM _ROI(k)+λ ₂* FlatIQM _ROI(k) realize low behaviour area feature detection.

7. the evaluating method of the video quality towards wireless network as claimed in claim 1, it is characterized in that in step 3), the spatial domain of whole section video of described structure and temporal signatures parameter are that Space-Time territory detected parameters is extracted the main quality influence parameter of 6 classes, and the subitem by 6 class parameters and comprehensive, embody video quality to be measured, the spatial domain and the temporal signatures parameter of whole section video of described structure comprise following concrete steps:

(1) the defined feature parameters C 1, and in order to the deviation of the average time domain energy of standard video, characteristic parameter C1 is defined as:

$\mathrm{AvgLR}\left[\mathrm{TI}\right]=1-\frac{|\mathrm{SrcAvgTI}-\mathrm{HrcAvgTI}|}{\mathrm{SrcAvgTI}}$

If the average time domain activity of reference video is SrcAvgTI, the average time domain activity of video to be measured is HrcAvgTI, and average time domain activity is then utilized following formula:

hod(i)＝imhist(|fn-fm|)

$\mathrm{HoD}(\mathrm{fn},\mathrm{fm})=\frac{\underset{i=0}{\overset{255}{\mathrm{\&Sigma;}}}i*\mathrm{hod}\left(i\right)}{N_{\mathrm{pix}}}$

Calculate AvgLR[TI] minimum value be 0, i.e. HrcAvgTI=0; Maximum is 1, i.e. HrcAvgTI=SrcAvgTI, AvgLR[TI] the defined parameters scope be [0-1], wherein 0 expression performance is the poorest, 1 expression performance is best;

(2) the defined feature parameters C 2, and in order to standard video frame losing incidence, characteristic parameter C2 is defined as:

$\mathrm{LR}\left[\mathrm{TI}\right]=1-\frac{\mathrm{Num}\_\mathrm{of}\_\mathrm{ZeroTi}}{\mathrm{Len}\_\mathrm{of}\_\mathrm{Section}}$

Wherein Len_of_Section is the detection segment length in the 1st behaviour area～N behaviour area, and detected video time domain activity to be measured is that zero frame number is Num_of_ZeroTi, normalized LR[TI] approximate more 1 performance of value is good more;

(3) the defined feature parameters C 3, and in order to the standard ratio of long continuous frame losing, characteristic parameter C3 is defined as:

$\mathrm{MaxLR}\left[\mathrm{TI}\right]=\frac{\mathrm{\&lambda;}}{{\mathrm{\&xi;}}^2+{(\mathrm{TI}\_\mathrm{Err}\_\mathrm{MaxLen})}^2}$

Wherein ξ and λ are positive integer, and TI_Err_MaxLen represents the longest detected lasting frame losing number, normalized MaxLR[TI] value approximate more 1 performance good more;

(4) the defined feature parameters C 4, and characteristic parameter C4 is:

$\mathrm{AvgLR}\left[\mathrm{SI}\right]=1-\frac{|\mathrm{MeanSrcIAM}-\mathrm{MeanHrcIAM}|}{\mathrm{MeanSrcIAM}}$

Wherein MeanSrcIAM and MeanHrcIAM are respectively the mean value of the spatial domain activity of the one section video that calculates.AvgLR[SI] minimum value be 0; Maximum is 1, and wherein 0 expression performance is the poorest, and 1 expression performance is best;

(5) the defined feature parameters C 5, are used to express estimate divide the lasting cycle that is lower than a certain tolerance value, and characteristic parameter C5 is:

$\mathrm{MaxLR}\left[\mathrm{SI}\right]=\frac{\mathrm{\&lambda;}}{{\mathrm{\&xi;}}^2+{(\mathrm{SI}\_\mathrm{Err}\_\mathrm{MaxLen})}^2}$

Wherein SI_Err_MaxLen represents to be lower than the lasting Cycle Length that expectation divides (0～1), and its value is distributed between (0,1), and wherein approximate more 1 performance is good more;

(6) the defined feature parameters C 6, are used to express comprehensive spatial domain quality branch, and to using weighted comprehensive between the different behaviour areas, and the spatial domain that finally obtains whole section video to be measured detects index, and characteristic parameter C6 is:

IQM[SI]＝ξ ₁*SI_IQM _High+ξ ₂*SI_IQM _Low

Wherein: ξ ₁+ ξ ₂=1, SI_IQM _HighAnd SI_IQM _LowRepresent the high behaviour area feature detection in the step (2) and the result of low behaviour area feature detection respectively;

(7) 6 characteristic parameters of step (1)～step (6) are carried out last comprehensive again, can obtain the comprehensive quality of the video that detects.Combined process is divided into 3 weights influence priority with 6 category feature parameters, and promptly C3 and C5 have the highest grade that influences; C2 and C6 have the grade that influences placed in the middle; And C1 and C4 to influence grade relative minimum, quality divides the pass of satisfying to be:

VideoScore＝k1*(C1、C4)+k2*(C2、C6)+k3*(C3、C5)

Wherein (Ci, Cj) expression influences grade, and ki represents to wait the weighted value of inter-stage, wherein k1＜k2＜k3, and k1+k2+k3=1.

8. towards the automatic evaluation system of the video quality of wireless network, it is characterized in that being provided with that information source is obtained and code device, deterioration emulation and analogue means and video quality automatic evaluation device;

The information source deriving means is used for the picture signal of acquisition camera picked-up and imports existing video file, the information source deriving means is provided with camera, memory and real-time process chip, camera output connects the input port of real-time process chip, memory links to each other with real-time process chip, and lead-in mode drives the input of camera intercepting video image by MCU in real time; File imports then and by PC download line video file is imported on the evaluation and test platform;

The emulation and the simulation of the video degradation form that deterioration emulation and analogue means are used to provide common, by emulation and the typical degradation feature of simulating in the transmission of acquisition wireless video, deterioration emulation and analogue means are made up of real-time process chip and peripheral storage, peripheral storage solidifies the degradation treatment model in advance, produce the deterioration form by transaction module, peripheral storage is connected with real-time process chip;

The video quality automatic evaluation device is provided with process chip, memory and peripheral auxiliary equipment, and memory connects with process chip, and the computer expert crosses the download line and connects with process chip.

9. the automatic evaluation system of the video quality towards wireless network as claimed in claim 8, it is characterized in that the real-time lead-in mode in the information source deriving means drives the input of camera intercepting video image by MCU, support at present wireless video commonly used 320 * 240, the video format of 176 * 144,352 * 288 3 kinds of resolution;

Chip in the real-time process chip available sources deriving means of deterioration emulation and analogue means, transaction module in deterioration emulation and the analogue means produce the deterioration form specifically comprise fuzzy pictures simulation, picture add the simulation of making an uproar, frame solidification simulation, frame-skip simulation, based on the wireless video Packet Loss Simulation of message switching scheme, simulate based on the wireless video error code emulation of circuit exchange mode and based on the point-to-point transmission of wireless network card 802.11b agreement.

10. the automatic evaluation system of the video quality towards wireless network as claimed in claim 8 is characterized in that the video quality automatic evaluation device is provided with peripheral auxiliary equipment, and peripheral auxiliary equipment comprises downloads the annex that line, power supply are used for the information source deriving means.