JP6402088B2 - Video quality estimation apparatus, video quality estimation method, and program - Google Patents

Description

  The present invention relates to a video quality estimation apparatus, a video quality estimation method, and a program, and in particular, a video for estimating a video quality value of video communication such as an IPTV service, a video distribution service, and a TV phone service performed via an IP network such as the Internet. The present invention relates to a quality estimation device, a video quality estimation method, and a program.

  In order to confirm that the video service is provided with good quality, the quality of the video experienced by the user is measured before or during service provision, and the quality of the video is confirmed and monitored. It is important to. Therefore, there is a need for a video quality quantification technique that can appropriately evaluate the quality of the video experienced by the user.

  Methods for quantifying video quality include a subjective quality evaluation method (for example, see Non-Patent Document 1) and a quality estimation technique (for example, see Non-Patent Documents 2 and 3). The subjective quality evaluation method requires a large number of users to evaluate video images in a facility where the evaluation environment (room illuminance, room noise, etc.) can be reproduced. Therefore, evaluation takes time. Therefore, development of a quality estimation technique for deriving a physical feature amount from a video signal and outputting a video quality value is being promoted (for example, see Non-Patent Documents 2 and 3).

  In the quality estimation technique described in Non-Patent Document 2, based on the five parameters of the entire video degradation amount, the degradation amount due to block distortion, the degradation amount due to blur, the local spatial degradation amount, and the freeze degradation amount, Video quality is estimated by multiple regression analysis. On the other hand, in Non-Patent Document 4, considering the characteristic that the user can easily perceive the degradation of the edge region, focusing on the edge amount, Non-Patent Document 2 is used as a parameter indicating the degradation amount of the entire video. It has been shown that video quality can be estimated with higher accuracy than PSNR (Peak Signal-to-Noise Ratio).

ITU-T recommendation P.910 "Subjective video quality assessment methods for multimedia applications" ITU-T recommendation J.247 "Objective perceptual multimedia video quality measurement in the presence of a full reference" ITU-T Recommendation J.341 "Objective perceptual multimedia video quality measurement of HDTV for digital cable television in the presence of a full reference" Kazuhisa Yamagishi, Lina Karam, Satoshi Okamoto, Takanori Hayashi, "Quality estimation model for stereoscopic video services," IEICE Technical Report, CQ2011-23, July 2011.

  As in the techniques described in Non-Patent Documents 2 and 3, in the conventional quality estimation technique, the weight of each quality estimation parameter constituting the quality estimation formula is a fixed value, from the low quality area to the high quality area All quality estimation parameters are adopted. However, depending on the quality estimation parameter, for example, as shown in FIG. 1A, there is a correspondence between the quality estimation parameter and the video quality in the low quality region, but the quality estimation parameter varies greatly in the high quality region. There are quality estimation parameters that have no correspondence. In the case of such a quality estimation parameter, it contributes to improving the quality accuracy of the low quality region, but the quality estimation accuracy is lowered in the high quality region.

  In addition, as shown in FIG. 1B, there are quality estimation parameters that contribute to improving the quality estimation accuracy in the medium quality region but reduce the quality estimation accuracy in the high quality / low quality region. Furthermore, as shown in FIG. 1 (c), there are quality estimation parameters that contribute to improving the quality estimation accuracy as the quality increases from the medium quality region to the high quality region. Therefore, in order to improve the quality estimation accuracy, it is necessary to select a quality estimation parameter that contributes to the improvement of the quality estimation accuracy according to the video quality, and to estimate the video quality using the selected quality estimation parameter. is there.

  The present invention has been made in view of the above points, and an object of the present invention is to provide a technique for accurately estimating the quality of a video experienced by a user who is viewing a video provided by a video service. .

According to an embodiment of the present invention, there is provided a video quality estimation device for estimating video quality experienced by a user with respect to a video of a degraded video signal based on a reference video signal and a degraded video signal,
Parameter calculation means for calculating a value of a quality estimation parameter used for calculating the video quality and a predetermined video quality from the reference video signal and the degraded video signal;
A quality calculating unit that calculates a contribution of the quality estimation parameter according to the predetermined video quality and calculates the video quality using a value of the quality estimation parameter multiplied by the contribution. Is provided.

In addition, according to the embodiment of the present invention, the video quality estimation apparatus for estimating the video quality experienced by the user with respect to the video of the degraded video signal is executed based on the reference video signal and the degraded video signal. A video quality estimation method,
A parameter calculating step for calculating a value of a quality estimation parameter used for calculating the video quality and a predetermined video quality from the reference video signal and the degraded video signal;
A quality calculation step of obtaining a contribution degree of the quality estimation parameter according to the predetermined video quality, and calculating the video quality using a value of the quality estimation parameter multiplied by the contribution degree. A video quality estimation method is provided.

  According to the embodiment of the present invention, there is provided a technique for accurately estimating the quality of a video experienced by a user who is viewing a video provided by a video service.

It is a figure which shows the example of a relationship between a quality estimation parameter and video quality. It is a figure for demonstrating the selection function of a quality estimation parameter. It is a figure which shows the structural example of the video quality estimation apparatus in embodiment of this invention. It is a figure which shows the structural example of the edge derivation | leading-out part in embodiment of this invention. It is a figure which shows the example of the selection function of the quality estimation parameter in embodiment of this invention.

  Hereinafter, an embodiment (this embodiment) of the present invention will be described with reference to the drawings. The embodiment described below is only an example, and the embodiment to which the present invention is applied is not limited to the following embodiment.

(Outline of the embodiment)
First, an outline of the present embodiment will be described. In the present embodiment, a video quality estimation apparatus including a parameter derivation unit and a quality integration unit, which will be described in detail later, is provided. The parameter deriving unit includes an edge deriving unit. The edge derivation unit includes a noise removal unit, an edge parameter derivation unit, and an edge-based quality estimation unit, and outputs an edge-based quality estimation value (hereinafter, an edge-based quality estimation value) indicating the contour of the image.

  The parameter deriving unit outputs a quality estimation value based on the edge, which is an output of the edge deriving unit, and other quality estimation parameter values (for example, PSNR calculated in J.247). The quality integrating unit outputs an integrated video quality estimated value based on the output of the parameter deriving unit. The integrated video quality estimation value is derived using a quality estimation formula that has a function that reduces the weight of quality estimation parameters that do not contribute to quality estimation, when quality estimation parameters used for quality estimation are selected according to the output of the edge deriving unit Is done.

  Here, an example of a method for selecting a quality estimation parameter used for quality estimation will be described. As shown in FIG. 1A, for quality estimation parameters that contribute to quality estimation in the low quality region but do not contribute to quality estimation in other regions, the selection function of the quality estimation parameter is shown in FIG. The setting is made as in a) so that the quality estimation parameter is not selected in an area other than low quality. The selection function here is a function that inputs a value of video quality and outputs a value indicating the weight of application of the quality estimation parameter (a value of 0 to 1 in this embodiment). The selection function shown in FIG. 2 (a) outputs 1 when the video quality is a value corresponding to the low quality region. This 1 means that the corresponding quality estimation parameter is applied as it is. On the other hand, when the video quality is a value corresponding to the medium quality region or the high quality region, 0 is output. This 0 means that the corresponding quality estimation parameter is not applied.

  As shown in FIG. 1B, for quality estimation parameters that contribute to quality estimation in the medium quality region but do not contribute to quality estimation in other regions, the selection function of the quality estimation parameter is shown in FIG. Set as in b) so that the quality estimation parameter is not selected in areas other than medium quality.

  In addition, as shown in Fig. 1 (c), as the quality increases from the medium quality region to the high quality region, the selection function as shown in Fig. 2 (c) is set for the quality estimation parameters that contribute to improving the quality estimation accuracy. In the high quality region, the quality estimation parameter is selected, but in the medium quality region, the weight of the quality estimation parameter is gradually reduced as the quality decreases (the quality estimation parameter is selected). The quality estimation parameter is not selected in the low quality region.

  That is, when the video quality is estimated using the three quality estimation parameters shown in FIG. 1, only the quality estimation parameter of FIG. 1A is selected in the low quality region in the quality estimation formula. In the middle quality region, the quality estimation parameters shown in FIGS. 1B and 1C are selected. In particular, the quality estimation parameters shown in FIG. 1C increase in weight as the quality increases. In the high quality region, only the quality estimation parameter shown in FIG. 1 (c) is selected.

  Here, in this embodiment, the output of the edge deriving unit, which is a quality estimation value based on the edge amount, is used as the video quality corresponding to the horizontal axis of FIG. In this way, by using the quality estimation parameter selection function as a function of the quality value estimated from the edge amount, the quality estimation parameter that contributes to the improvement of the quality estimation accuracy can be selected by the quality estimation value based on the edge amount. Thereby, the quality estimation parameter used for quality estimation is selected according to the output of the edge deriving unit, and a quality estimation formula for reducing the weight of the quality estimation parameter that does not contribute to quality estimation is realized.

  Note that, as the video quality on the horizontal axis in FIG. 2, the use of a quality estimation value based on the edge amount is an example, and a quality estimation value other than the quality estimation value based on the edge amount may be used. Hereinafter, the configuration and operation of the video quality estimation apparatus according to the present embodiment will be described in more detail.

(Apparatus configuration, function overview)
FIG. 3 shows a configuration example of the video quality estimation apparatus 1 according to the present embodiment. As shown in FIG. 3, the video quality estimation apparatus 1 includes a parameter derivation unit 2 and a quality integration unit 3. The parameter deriving unit 2 includes an edge deriving unit 21.

  As shown in FIG. 3, the video quality estimation apparatus 1 receives the reference video signal R1 and the degraded video signal P1 of the evaluation target video, and outputs an integrated video quality estimated value Vqe. Here, the reference video signal R1 is a video signal that has not undergone encoding or network transmission. The deteriorated video signal P1 is a video signal whose quality has deteriorated as a result of the reference video signal R1 undergoing encoding, network transmission, or the like.

  FIG. 4 is a diagram illustrating a configuration example of the edge deriving unit 21 in the parameter deriving unit 2. As shown in FIG. 4, the edge deriving unit 21 includes a noise removing unit 211, an edge parameter deriving unit 212, and an edge-based quality estimating unit 213. The edge deriving unit 21 receives the reference video signal R1 and the deteriorated video signal P1, and outputs a quality estimation value Vedge.

  The parameter deriving unit 2 outputs the output Vedge of the included edge deriving unit 21 and other quality estimation parameter values Vp1 to Vpn (n is the number of parameters). Specifically, by comparing the reference video signal and the degraded video signal, the overall video degradation amount described in Non-Patent Document 2 (ITU-T recommendation J.247), the degradation amount due to block distortion, The amount of deterioration, local spatial deterioration, freeze deterioration, etc. are obtained. Note that the overall video degradation amount, the amount of degradation due to block distortion, and the amount of degradation due to blurring are equivalent to the amount of degradation due to encoding, and the amount of local spatial degradation and freeze degradation are the amount of degradation due to packet loss. Equivalent to.

  The quality integrating unit 3 is a quality calculating unit that calculates and outputs an integrated video quality estimated value Vqe that is a quality estimation result of the degraded video signal based on the outputs Vedge and Vp1 to Vpn of the parameter deriving unit 2. The integrated video quality estimation value Vqe is derived using a quality estimation formula in which each quality estimation parameter value (Vedge, Vp1 to Vpn) used for quality estimation is selected according to the output Vedge of the edge deriving unit 21. Note that the integrated video quality estimated value Vqe corresponds to the video quality that the user feels for the video of the degraded video signal.

  The video quality estimation apparatus 1 according to the present embodiment can be realized, for example, by causing one or a plurality of computers to execute a program describing the processing content described in the present embodiment. That is, the function of the video quality estimation apparatus 1 is realized by executing a program corresponding to the process executed by the video quality estimation apparatus 1 using hardware resources such as a CPU and a memory built in the computer. Is possible. The above-mentioned program can be recorded on a computer-readable recording medium (portable memory or the like), stored, or distributed. It is also possible to provide the program through a network such as the Internet or electronic mail.

(Operation of each part)
Next, the processing operation of each part constituting the video quality estimation apparatus 1 will be described in detail.

<Edge Deriving Unit 21>
First, the operation of the edge deriving unit 21 (FIG. 4) will be described in detail. The noise removing unit 211 in the edge deriving unit 21 performs noise removal for each of the reference video signal R1 and the degraded video signal P1, and outputs the reference video signal R1 ′ and the degraded video signal P1 ′.

  As a noise removal method, for example, a method of performing sub-sampling by applying a low-pass filter is used. The low-pass filter in the present embodiment is realized by setting the average value of 2 × 2 vertical and horizontal pixel values as the respective pixel values. However, realizing the low-pass filter by averaging 2 × 2 pixel values is an example, and the low-pass filter may be realized by setting the average value of nxn pixel values such as 3 × 3. In subsampling, it means that a 2 × 2 pixel value is handled as one pixel. That is, a video having a resolution of 3840x2160 is converted into a video having a resolution of 1920x1080. Similar to the above-described low-pass filter, a pixel value of 3 × 3 or nxn may be handled as one pixel.

  The edge parameter deriving unit 212 in the edge deriving unit 21 receives the reference video signal R1 ′ and the degraded video signal P1 ′ after noise removal, and derives an edge amount (E). Specifically, first, the difference value Fdif between the pixels of the reference video signal R1 ′ after noise removal and the degraded video signal P1 ′ is obtained using the following Equation 1. Next, a Sobel filter is applied to Fdif for each frame to derive an edge amount (SIdif) for each frame (Formula 2). Finally, the edge amount (E) is derived from the average value of SIdif values of all frames (Formula 3).

Fdif (k, i, j) = R1 ′ (k, i, j) −P1 ′ (k, i, j) (Formula 1)
SIdif (k) = stdspace [Sobel (Fdif (k, i, j))] (Formula 2)

式１〜３のkはフレーム番号、iは画素の行番号、jは画素の列番号、Nは総フレーム数を表す。なお、エッジ量の算出方法は上記の算出方法に限られず、他の方法でエッジ量を算出してもよい。

In Equations 1 to 3, k is a frame number, i is a pixel row number, j is a pixel column number, and N is the total number of frames. Note that the edge amount calculation method is not limited to the above calculation method, and the edge amount may be calculated by another method.

  The edge-based quality estimation unit 213 receives the output E of the edge parameter deriving unit 212 as an input, and derives a quality estimation value Vedge. Specifically, the quality estimation value Vedge based on the edge amount is derived by a non-linear model shown in Expression 4 in consideration of human perceptual characteristics.

Vedge = a1 + (a2-a1) / (1+ (E / a3) ^ a4) (Formula 4)
It is assumed that the coefficients a1 to a4 in Expression 4 are derived on the basis of the subjective evaluation experiment database that has been performed and are held in a storage unit (storage means such as a memory) (not shown) of the video quality estimation apparatus 1.

<Parameter derivation unit 2>
Next, the operation of the parameter deriving unit 2 (FIG. 3) will be described in detail. The parameter deriving unit 2 calculates and outputs quality estimation parameter values Vp1 to Vpn (n is the number of parameters). The parameter deriving unit 2 also outputs Vedge obtained by the edge deriving unit 21. Specifically, for example, the amount of deterioration of the entire video described in Non-Patent Document 2, the amount of deterioration due to block distortion, the amount of deterioration due to blur, the amount of local spatial deterioration, the amount of freeze deterioration, and the like are obtained. Output as quality estimation parameter value.

  Moreover, it is good also considering the result of having estimated quality using the quality estimation formula described in the nonpatent literature 2 as one of the quality estimation parameter values. That is, in this case, as described in Non-Patent Document 2, the parameter deriving unit 2 determines the overall video degradation amount, the degradation amount due to block distortion, the degradation amount due to blur, the local spatial degradation amount, and the freeze degradation. A quality estimation value is calculated by performing weighted addition on the five feature quantities of the quantity, and is output as one of the quality estimation parameter values. It is assumed that the coefficients in the quality estimation formula used here are held in the storage unit. The coefficient is derived in advance by multiple regression analysis.

  Here, the deterioration amount of the entire image is a feature amount indicating the steady deterioration occurring in the entire image from the difference between the reference image and the deteriorated image. The amount of deterioration due to block distortion is a feature amount indicating the occurrence of block distortion obtained by taking the ratio of deterioration occurring in the horizontal and vertical directions and other deterioration. The amount of deterioration due to blur is a feature amount indicating blur of motion of the video for each block, which is obtained by comparing inter-frame difference values for each block of the reference / degraded video. Local spatial degradation is a feature that indicates discrete block distortion obtained by calculating the magnitude of the fluctuation of a block with a large inter-frame difference value for each block of the reference / degraded video. It is. The freeze deterioration amount is a feature amount indicating a decrease in frame rate or freeze deterioration obtained by performing weighting based on human visual characteristics on the time for which video is continuously displayed.

<Quality Integration Department 3>
Next, the operation of the quality integration unit 3 (FIG. 3) will be described in detail.

  Based on the output Vedge of the edge deriving unit 21 and the outputs Vp1 to Vpn of the parameter deriving unit 2, the quality integrating unit 3 calculates and outputs an integrated video quality estimated value (Vqe) by Equation 5.

式５において、wl(l=0,1,…,n)を各品質推定パラメータ(Vedge、Vpl)に対する重みとし、bl(Vedge)を品質推定パラメータの選択関数とし、c(Vedge)を補正項とする。式５に示すように、本実施の形態では、Vedgeも品質推定パラメータの１つとして使用されており、これに対応する重みがw0であり、選択関数がb0(Vedge)である。Vpl(l=1,…,n)に対する重みはwl(l=1,…,n)であり、選択関数はbl(Vedge) (l=1,…,n)である。なお、選択関数は、映像品質（Vedge）に応じて、品質推定パラメータがどの程度、品質推定に寄与するかを示す値を出力する関数である。

In Equation 5, wl (l = 0,1, ..., n) is a weight for each quality estimation parameter (Vedge, Vpl), bl (Vedge) is a selection function for the quality estimation parameter, and c (Vedge) is a correction term. And As shown in Equation 5, in this embodiment, Vedge is also used as one of the quality estimation parameters, the corresponding weight is w0, and the selection function is b0 (Vedge). The weight for Vpl (l = 1, ..., n) is wl (l = 1, ..., n), and the selection function is bl (Vedge) (l = 1, ..., n). The selection function is a function that outputs a value indicating how much the quality estimation parameter contributes to the quality estimation according to the video quality (Vedge).

  Here, an example of a setting method of the quality estimation parameter selection function bl (Vedge) (l = 0, 1,..., N) is shown. For example, as shown in FIG. 5 (a), the selection function bl (Vedge) of the quality estimation parameter selected when Vedge> t0 is shown in Expression 6.

・ When Vedge ≤ t0, bl (Vedge) = 0 (Formula 6)
・ When Vedge> t0, bl (Vedge) = 1
As shown in FIG. 5B, the quality estimation parameter selection function bl (Vedge) that gradually increases the contribution to the quality estimation from the medium quality region to the high quality region is shown in Equation 7.

bl (Vedge) = d1 + (d2 − d1) / (1+ (Vedge / d3) ^ d4) (Equation 7)
Equation 7 is an example of a function that draws a curve as shown in FIG. The selection function of the quality estimation parameter whose contribution to quality estimation gradually increases from the medium quality region to the high quality region is not limited to Equation 7, and other equations may be used. In addition, by adjusting the constant in Expression 7, it is possible to set a selection function that outputs 0 until t0 and outputs 1 after t0, as shown in FIG.

  Further, as shown in FIG. 5C, a quality estimation parameter selection function bl (Vedge) that contributes to quality estimation only in the middle quality region is shown in Equation 8.

・ When Vedge ≤ t1, bl (Vedge) = 0 (Formula 8)
・ When t1 <Vedge <t2, bl (Vedge) = 1
・ When t2 ≤ Vedge, bl (Vedge) = 0
The selection function bl (Vedge) shown as the above Equations 6 to 8 is merely an example, and is not limited thereto. For example,
・ When Vedge ≤ t0, bl (Vedge) = 1
・ When Vedge> t0, bl (Vedge) = 0
As described above, it is possible to use the one that gives the opposite effect to (Equation 6), or the one that gives the opposite of (Equation 7). In the present embodiment, an appropriate selection function bl (Vedge) corresponding to the quality estimation parameter characteristic (for example, FIG. 1) is set for each quality estimation parameter, stored in the storage unit of the video quality estimation apparatus 1, Used by the quality integration unit 3.

  As a result, the quality estimation parameter used for quality estimation is selected according to the quality estimation value Vedge based on the edge amount, and the quality estimation parameter selection function bl (Vedge) that reduces the weight of the quality estimation parameter that does not contribute to quality estimation is realized Is done. Since bl (Vedge) outputs a value indicating how much the quality estimation parameter contributes to quality estimation, the output value of bl (Vedge) may be referred to as a contribution degree.

  Here, information such as wl, c, bl in Equation 5, t0 in Equation 6, d1 to d4 in Equation 7, t1, t2 in Equation 8, etc. are derived based on the subjective evaluation experiment database that has already been performed, and the video quality The quality integrating unit 3 reads out the above coefficients, functions, and the like from the storage unit, performs calculation of Expression 5, and outputs Vqe.

  In the present embodiment, as shown in Equation 5, video quality is estimated by multiplying each quality estimation parameter by a selection function suitable for the quality estimation parameter, but a plurality of quality estimation parameters are integrated. The value may be multiplied by the selection function and integrated with the quality value based on the edge amount. For example, as described above, J.247 is a technology for estimating quality from five parameters, and the quality derived from these five parameters is multiplied by a selection function as a quality estimation parameter and integrated with a quality value based on the edge amount. The final quality value may be estimated.

  More specifically, in the parameter deriving unit 2, as the quality parameter Vp1, the five parameters of “the entire image degradation amount, the degradation amount due to block distortion, the degradation amount due to blur, the local spatial degradation amount, and the freeze degradation amount” "Video quality estimation result obtained by weighting and adding", that is, "result output using technology of J.247". That is, Vp1 is calculated by the following formula 9.

Vp1 = a1 x (deterioration amount of the entire image) + a2 x (degradation amount due to block distortion) + a3 x (degradation amount due to blur) + a4 x (local spatial degradation amount) + a5 x (freeze degradation amount) + a6 (Formula 9) (a1 to a6 are constants)
Then, using the output Vedge of the edge deriving unit 21 and the result Vp1 output by J.247 of the parameter deriving unit 2, the quality integrating unit 3 uses the integrated video quality estimation value (number of parameters n = 1) according to Equation 5 (number of parameters n = 1). Vqe) is output. That is, Vqe is calculated by the following formula 10.

Vqe = w0 x b0 (Vedge) x Vedge + w1 x b1 (Vedge) x Vp1 + c (Vedge) (Equation 10)
As described above, according to the present embodiment, according to the quality estimation value Vedge based on the edge amount, the quality estimation parameter that contributes to the improvement of the quality estimation accuracy is selected, and the video quality is selected using the selected quality estimation parameter. Can be estimated. Thereby, quality estimation accuracy improves.

  Therefore, the video communication service provider can easily determine whether the service being provided maintains a certain level of quality for the user, and can grasp the actual quality of the service being provided in real time. It becomes possible to manage.

(Summary of embodiment)
According to the present embodiment, there is provided a video quality estimation apparatus for estimating video quality experienced by a user with respect to a video of a degraded video signal based on a reference video signal and a degraded video signal, A parameter calculation means for calculating a quality estimation parameter value used for calculating the video quality and a predetermined video quality from the degraded video signal, and a degree of contribution of the quality estimation parameter according to the predetermined video quality And a quality calculation means for calculating the video quality using the value of the quality estimation parameter multiplied by the degree of contribution. The parameter deriving unit 2 is an example of a parameter deriving unit, and the quality integrating unit 3 is an example of a quality calculating unit.

  A selection function that outputs a contribution of a quality estimation parameter corresponding to the predetermined video quality is predetermined for each quality estimation parameter, and the selection function is stored in a storage unit of the video quality estimation device, and the quality The calculating means may calculate the contribution by inputting the predetermined video quality to a selection function corresponding to the quality estimation parameter calculated by the parameter calculating means.

  The predetermined video quality is, for example, a quality calculated based on an edge amount obtained from a difference value between the reference video signal and the degraded video signal.

  The quality estimation parameter value may include an overall video degradation amount, a degradation amount due to block distortion, a degradation amount due to blur, a local spatial degradation amount, and a freeze degradation amount. The quality estimation parameter value is obtained by weighted addition of the overall video degradation amount, the block distortion degradation amount, the blur-related degradation amount, the local spatial degradation amount, and the freeze degradation amount. It is good also as including.

  As mentioned above, although the Example of this invention was explained in full detail, this invention is not limited to such specific embodiment, In the range of the summary of this invention described in the claim, various deformation | transformation・ Change is possible.

DESCRIPTION OF SYMBOLS 1 Image | video quality estimation apparatus 2 Parameter deriving part 3 Quality integration part 21 Edge deriving part 211 Noise removal part 212 Edge parameter deriving part 213 Quality estimation part based on edge
R1 reference video signal
P1 Degraded video signal
R1 'Reference video signal after noise removal
P1 'Degraded video signal after noise removal
Fdif Pixel difference value of reference video signal R1 'and degraded video signal P1' after noise removal
SIdif Fdif multiplied by a Sobel filter
E Edge amount
k Frame number
i pixel row number
j Pixel column number
N total frames
p0 ~ pn 0 ~ n quality estimation parameters
Vqe integrated video quality estimate
Vedge Quality value estimated from edge amount
Vp1 to Vpn 1 to n quality estimation parameter values
a1 to a4 coefficient of function for deriving Vedge
wl (l = 0,1,…, n + 1) Weight of quality estimation formula for deriving Vqe
bl (Vedge) (l = 0,1, ..., n + 1) Quality estimation parameter selection function in the quality estimation formula to derive Vqe
c (Vedge) Correction term for quality estimation formula to derive Vqe
d1-d4 Quality estimation parameter selection function bl (Vedge) coefficient
t0 to t2 Quality estimation parameter selection function bl (Vedge) threshold

Claims (8)

A video quality estimation device for estimating a video quality experienced by a user with respect to a video of a degraded video signal based on a reference video signal and a degraded video signal,
Parameter calculation means for calculating a value of a quality estimation parameter used for calculating the video quality and a predetermined video quality from the reference video signal and the degraded video signal;
A quality calculating unit that calculates a contribution of the quality estimation parameter according to the predetermined video quality and calculates the video quality using a value of the quality estimation parameter multiplied by the contribution. A video quality estimation device. A selection function that outputs a contribution of a quality estimation parameter according to the predetermined video quality is predetermined for each quality estimation parameter, and the selection function is stored in a storage unit of the video quality estimation device,
The said quality calculation means calculates the said contribution by inputting the said predetermined | prescribed video quality to the selection function corresponding to the said quality estimation parameter calculated by the said parameter calculation means. The video quality estimation apparatus described. The predetermined video quality is a quality calculated based on an edge amount obtained from a difference value between the reference video signal and the degraded video signal. Video quality estimation device. The value of the quality estimation parameter includes an overall video degradation amount, a block distortion degradation amount, a blur degradation amount, a local spatial degradation amount, and a freeze degradation amount. The video quality estimation apparatus according to any one of the preceding claims. The value of the quality estimation parameter includes video quality obtained by weighted addition of the degradation amount of the entire video, the degradation amount due to block distortion, the degradation amount due to blur, the local spatial degradation amount, and the freeze degradation amount. 5. The video quality estimation apparatus according to claim 1, wherein the video quality estimation apparatus is any one of claims 1 to 4. A video quality estimation method executed by a video quality estimation device for estimating video quality experienced by a user with respect to a video of a degraded video signal based on a reference video signal and a degraded video signal,
A parameter calculating step for calculating a value of a quality estimation parameter used for calculating the video quality and a predetermined video quality from the reference video signal and the degraded video signal;
A quality calculation step of obtaining a contribution degree of the quality estimation parameter according to the predetermined video quality, and calculating the video quality using a value of the quality estimation parameter multiplied by the contribution degree. A video quality estimation method. A selection function that outputs a contribution of a quality estimation parameter according to the predetermined video quality is predetermined for each quality estimation parameter, and the selection function is stored in a storage unit of the video quality estimation device,
In the quality calculation step, the video quality estimation device calculates the contribution by inputting the predetermined video quality to a selection function corresponding to the quality estimation parameter calculated in the parameter calculation step. The video quality estimation method according to claim 6, wherein the video quality is estimated.   The program for functioning a computer as each means in the video quality estimation apparatus of any one of Claims 1 thru | or 5.

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