Patent Description:
In some cases, a camera device used in a monitoring system may fail to capture a video-image due to changes in the photographing environment such as a camera failure or a lighting failure, camera tampering such as blocking or covering the lens of a camera, or the like.

Conventionally, the camera device may automatically detect some failures, but cannot automatically detect all abnormalities in image data. As an example, in a case where a dark image is outputted from the camera device, it is difficult to distinguish whether a camera failure occurs or an image of a dark object is captured. Meanwhile, if there is an observer, the observer can comprehensively determine the presence or absence of abnormalities while estimating the cause of the abnormality by empirically understanding the photographing environment.

In the related art, there is disclosed a solid-state pick-up device which is capable of detecting a failure, such as a case where no signal for the failure is outputted from an image sensor, even under the dark environment (see, e.g., Patent Document <NUM> or <NUM>).

In a large-scale monitoring system, it is difficult for the observer to visually and immediately detect video-image abnormalities of all camera devices. Further, visual observation of the observer is not suitable practically for a small-scale monitoring system which is required to save manpower. In the case of a surveillance system for an unmanned store, a recorded video-image may be checked by the observer only when an incident has occurred without usually monitoring the store. In this case, video-image abnormalities may not be detected until the recorded video-image is visually checked at the time of the occurrence of the incident, which is problematic.

However, a damage of the camera device or the like needs to be detected more reliably and more rapidly, as a potential threat to be monitored, by using other camera devices.

<CIT> discloses a self-contained monitoring camera system including a monitoring camera, a recording medium, a medium recording management unit and an image diagnosis processing unit. Malfunction is detected based on histogram analysis and on motion analysis.

In view of the above, the present invention aims to automatically detect an abnormality of image data caused by a failure of a camera device or the like by using a low-load video signal processing.

While the invention is defined in the independent claims, further aspects of the invention are set forth in the dependent claims, the following description and the drawings.

According to the present invention, it is possible to efficiently detect video-image abnormalities from a plurality of camera devices without an observer's visual observation.

<FIG> shows a schematic configuration of a monitoring system according to one embodiment of the present invention.

A monitoring system <NUM> is configured such that a plurality of internet protocol (IP) cameras (network cameras) <NUM> are connected to a server <NUM> and a recording device <NUM> through a network <NUM>.

Each IP camera <NUM> is fixedly installed to capture a video-image in a monitoring area <NUM>. Specifically, the IP camera <NUM> captures a moving object such as a human being <NUM> and a stationary object such as a street lamp <NUM>, for example.

A server <NUM> transmits, for example, an instruction of video-image delivery to the IP camera <NUM> to display an alarm and/or the video-image received from the IP camera <NUM> on a display device <NUM> connected to the server <NUM>. In addition, the server <NUM> performs monitoring of an operation of the IP camera <NUM>, remote controlling of the light lamp built in the IP camera <NUM>, integration of received alarms and the like. Further, the server <NUM> provides a screen <NUM> for setting a plurality of measurement areas <NUM> and <NUM> which will be described later by a manipulation of the user.

The recording device <NUM> includes a large-capacity storage medium such as a hard disk drive (HDD) and is configured to record video data delivered from the IP camera <NUM> at all times or in response to the alarm. Further, in response to a request from the server <NUM>, the recording device <NUM> reads out the recorded video data to transmit it to the server <NUM>.

The display device <NUM> is a general computer display. <FIG> shows a state in which the display device <NUM> displays the measurement area setting screen <NUM>. In the measurement area setting screen <NUM>, the measurement areas <NUM> and <NUM> which are arbitrarily set by the user are displayed as rectangles.

<FIG> is a block diagram of the IP camera <NUM>.

The IP camera <NUM> includes an image sensor unit <NUM>, an encoding unit <NUM>, a measurement unit <NUM>, a video delivery unit <NUM>, and an alarm management unit <NUM>.

The image sensor unit <NUM> is a generally available camera module and includes an image pickup unit <NUM>, an automatic gain control (AGC) unit <NUM>, and an adjustment unit <NUM> to output digital data of the captured video-image.

The image pickup unit <NUM> is configured to photoelectrically convert light condensed by a lens unit <NUM> to output the converted electrical signal to the AGC unit <NUM> as a raw image format (RAW) signal <NUM>.

The AGC unit <NUM> amplifies the RAW signal <NUM> to a predetermined level to output the amplified RAW signal to the adjustment unit <NUM>. The image pickup unit <NUM> may be a complementary metal oxide semiconductor (CMOS) image sensor, and the AGC unit <NUM> may be provided in the image pickup unit <NUM> if the image pickup unit <NUM> outputs the analog-to-digital converted RAW signal.

The adjustment unit <NUM> is a central processing unit of the IP camera <NUM> which performs an image capturing control or an image processing such that the IP camera <NUM> outputs a desirable video-image. The adjustment unit <NUM> includes a lens control unit <NUM>, a gradation correction unit <NUM>, and a miscellaneous correction unit <NUM>.

The lens control unit <NUM> is configured to control the iris and focus of the lens unit <NUM> based on image data outputted from the AGC unit <NUM>.

A gradation correction (equalization) for the image data outputted from the AGC unit <NUM> is performed by the gradation correction unit <NUM>. A color correction, an adaptive noise-filtering, a flicker removal, an edge enhancement and the like for the image data outputted from the AGC unit <NUM> are performed by the miscellaneous correction unit <NUM> to output the corrected image data as digital image data.

The encoding unit <NUM> is configured to encode the digital image data outputted from the image sensor unit <NUM> to thereby generate encoded image data of a predetermined format such as joint photo graphic experts group (JPEG), H. <NUM> or the like.

The video delivery unit <NUM> is configured to deliver the encoded image data by using user datagram protocol/internet protocol (UDP/IP) or the like.

The measurement unit <NUM> is configured to detect a failure through a method which will be described later and output failure detection information <NUM> and <NUM> based on the digital image data <NUM> outputted from the image sensor unit <NUM>. The measurement unit <NUM> includes a storage unit <NUM> which stores the digital image data <NUM> or values calculated from the digital image data <NUM>.

The failure detection information <NUM> is included in the encoded image data by the video delivery unit <NUM> and is delivered through a network <NUM>.

The failure detection information <NUM> is managed by the alarm management unit <NUM> and is sequentially outputted through the network <NUM> as an independent alarm notification <NUM>.

With regard to the failure detection information <NUM>, the failure detection information <NUM> is integrated into the encoded image data, so that the failure detection information <NUM> can be reliably delivered and recorded even with the conventional device. With regard to the failure detection information <NUM>, there is an advantage that an alarm can be recognized at the receiving side without interpreting contents of the encoded image data. Therefore, in this example, the information is redundantly delivered.

<FIG> shows a structure of encoded image data used in the monitoring system <NUM> of the present embodiment.

Encoded image data <NUM>, which is generally provided on a picture basis or the like, includes header information <NUM> and image data <NUM>.

The image data <NUM> is a bit stream generated by the encoding such as two-dimensional Huffman coding, context adaptive binary arithmetic coding (CABAC) or context adaptive variable length coding (CAVLC) in the encoding unit <NUM>.

The header information <NUM> stores therein attributes (e.g., picture size and so on) of the image data in unit and parameters (e.g., quantum conversion table and so on) used in the encoding, and usually has a user data area <NUM> which can be arbitrarily used. In this example, video-image abnormality detection information <NUM> is stored in the user data area <NUM>.

The failure detection information <NUM> may be an example of the video-image abnormality detection information <NUM>. The video-image abnormality detection information <NUM> may include a presence or absence (or probability) of detection, a detection type, and detection area coordinates, which are provided to correspond to the number of the detection areas.

Hereinafter, there will be described several algorithms of the failure detection by the measurement unit <NUM>. These algorithms may be implemented alone or in combination. In the latter case, the algorithms may be implemented in parallel. Alternatively, one algorithm may be dependent on another algorithm such that upon detecting a failure by executing one algorithm, another algorithm is executed so as to double-check the detection result.

<FIG> is a flowchart of algorithm A. Algorithm A is used for determining that an abnormality has occurred when there is a change in a mean value of a luminance signal within a measurement area. The measurement area and a set range (threshold) for determining the abnormality when there is a change in the mean value of the luminance signal are determined in advance to correspond to, e.g., those of the street lamp <NUM> which is expected to provide constant illuminance. For example, when the street lamp <NUM> is in a turn-on state in <FIG>, it is assumed that there is no luminance change in an image capturing area of the street lamp <NUM>, so that it can be employed as the measurement area <NUM>.

The measurement unit <NUM> acquires the latest digital image data of one picture that is outputted from the image sensor unit <NUM> (step S401), and the process proceeds to step S402.

In step S402, a removal or a masking of the moving object in the measurement area is performed by using digital image data (background image) stored in the storage unit <NUM>, and the process proceeds to step S403. Alternatively, when the moving object is detected in the measurement area, the process for the corresponding picture may be aborted. The removal of the moving object can be performed by a well-known technique which is available in the art.

In step S403, the mean value of the luminance signal in the measurement area is calculated, and the process proceeds to step S404.

In step S404, the mean value of the luminance signal is converted into absolute illuminance and it is determined whether or not the mean value of the luminance signal is out of the set range. If it is determined that the mean value of the luminance signal is out of the set range (YES in step S404), then the process proceeds to step S405. If it is determined that the mean value of the luminance signal is within the set range (NO in step S404), then the process returns to step S401. The absolute illuminance in the measurement area is obtained by multiplying the mean value of the luminance signal by a coefficient determined by an AGC gain, an exposure time (shutter speed) and an aperture value (f-number).

In step S405, since the mean value of the luminance signal is out of the set range, it is determined that there is an abnormality in the digital image data <NUM> and the process returns back to step S401 after the failure detection information <NUM> is notified (outputted) to the video delivery unit <NUM> and the failure detection information <NUM> is notified (outputted) to the alarm management unit <NUM>.

Upon receiving the failure detection information <NUM>, the alarm management unit <NUM> transmits an alarm to the server <NUM>. The server <NUM> performs a process of, e.g., displaying a warning message upon receiving the alarm. Further, since the failure detection information <NUM> is recorded together with the video-image in the recording device <NUM>, it becomes possible to check video-images before and after the video-image having the abnormality by searching information on the video-image abnormality in the recorded video-image.

In algorithm A, since an accurate operation can be performed only when the street lamp <NUM> is in a turn-on state, a measurement schedule is set to correspond to a time period during which the street lamp is turned on. Meanwhile, in a case where the street lamp <NUM> is a lighting device which may cause a flickering, the mean value of the luminance signal is preferably subjected to averaging even in the time domain as many as the number of pictures corresponding to a flickering cycle. The flickering cycle can be detected in the miscellaneous correction unit <NUM> or the like by techniques well known in the art.

According to algorithm A, it is possible to detect the covering of the lens unit <NUM> that is caused by, e.g., mischief or the like.

<FIG> is a flowchart of algorithm B. Algorithm B is used for determining that an abnormality has occurred when there is no change in a mean value of a luminance signal within a measurement area. In this example, in the imaging angle of view (field of view), there is an object with illuminance (luminance) being changed (blinking) at a known timing. The measurement area and a predetermined time period for determining the abnormality and a specific value for the amount of change in the mean value of the luminance signal are determined in advance so as to correspond to those for the object. The object includes an object that is illuminated by the light lamp (visible ray or near-infrared ray) equipped in the IP camera <NUM> or an external lighting device operated with the AC power supply frequency. Alternatively, the object may be the light lamp itself equipped in another IP camera <NUM>.

The measurement unit <NUM> acquires the latest digital image data of one picture that is outputted from the image sensor unit <NUM> (step S501), and the process proceeds to step S502.

In step S502, the mean value of the luminance signal in the measurement area is calculated, and the process proceeds to step S503.

In step S503, it is determined whether or not the amount of change in the mean value is smaller than or equal to the specific value by comparing a mean value of the luminance signal of the digital image data <NUM> with a mean value of the luminance signal of the previous digital image data stored in the storage unit <NUM>. If the amount of change in the mean value is smaller than or equal to the specified value (YES in step S503), it is determined that there is a potential abnormality so that the process proceeds to step S504. If the amount of change in the mean value is greater than the specified value (NO in step S503), it is determined as normal so the process returns back to step S501.

In step S504, it is determined whether or not a state where there is no change in the mean value of the luminance signal (that is, the amount of change is smaller than or equal to the specific value) is maintained for the predetermined time period. If the no-change state is maintained for the predetermined time period (YES in step S504), it is determined as abnormal so that the process proceeds to step S505. If the no-change state is not maintained for the predetermined time period (NO in step S504), the process returns back to step S501. The predetermined time period is set based on the flickering period, a blinking pattern period of the lighting device itself, or a time period according to an error in the known change timing.

In step S505, it is determined that there is an abnormality in the digital image data <NUM> and the process returns back to step S501 after the failure detection information <NUM> is notified (outputted) to the video delivery unit <NUM> and the failure detection information <NUM> is notified (outputted) to the alarm management unit <NUM>.

In this example, by using the variation of the lighting device causing the flickering, it is determined as abnormal when the flickering is no longer observed. Therefore, it is suitable for an indoor environment. When no human being is present so that the lighting device is turned off, the near-infrared lighting device equipped in the IP camera <NUM> or the like is controlled from the server <NUM> to be turned on, so that the operation of the failure detection can be continued. Alternatively, if it is preferable to optionally perform the failure detection, the algorithm B may be implemented only when the lighting device is turned on and off.

Alternatively, it is preferable to use the measurement area <NUM>, which is specified as an area in which the luminance of the image is frequently changed (e.g., a hallway where there is a movement of the human. being <NUM>). In this case, the time period (e.g., commuting time zone) during which the human being passes by is used to set the measurement schedule.

<FIG> are schematic views for explaining a principle of algorithm C and <FIG> is a flowchart of algorithm C. Algorithm C relates to a method for automatically setting the measurement area mentioned above. Specifically, in the method, an image frame (picture) is divided into a plurality of measurement areas, • and areas suitable for the measurement areas <NUM> and <NUM> are specified by observing an aspect of the luminance change (a period or a width thereof) in a normal state.

Graphs shown in <FIG> represent a change of the mean value of the luminance signal over time in each of the measurement areas <NUM> to <NUM>, where a horizontal axis indicates time, and a vertical axis indicates the mean value of the luminance signal.

As shown in <FIG>, the measurement unit <NUM> measures the change of the mean value of the luminance signal in each measurement area for a predetermined time period (e.g., one day) and stores the measurement result in the storage unit <NUM> (step S801). The plurality of the measurement areas are obtained by dividing the digital image data into N × M (e.g., <NUM> (rows) × <NUM> (columns)) areas as shown in <FIG>. The IP camera <NUM> itself sets the measurement period of, e.g., about one day after the installation thereof.

In step S802, the statistics of the mean value of the luminance signal are obtained for each measurement area. The statistics include a longest time period during which a state where there is no change in the mean value of the luminance signal is maintained, and the minimum and maximum values of the mean value of the luminance signal, and these statistics are stored in the storage unit <NUM> after the predetermined time period has lapsed. Steps S801 and S802 are generally performed in parallel. For example, the longest time period can be obtained by a method in which the measurement unit <NUM> counts a time period for no change and resets the count value to zero when there is a change, and the measurement unit <NUM> overwrites the count value when the count value is greater than a previous count value stored in the storage unit <NUM>. Preferably, when the overwriting is carried out, a previous reset time may be stored so that the previous reset time (the one before the current reset causing the overwriting) and the current reset time are also overwritten and stored. Further, "the state where there is no change in the mean value of the luminance signal' may indicate that the change is less than <NUM>% of a rating value.

In step S804, it is determined, for each measurement area, whether or not a level change constantly occurs in the mean value of the luminance signal. For a measurement area in which the level change constantly occurs (YES in step S804), a process in step S807 is performed. For a measurement area in which the level change does not constantly occur (NO in step S804), a process in step S805 is performed. Here, when the longest time period obtained in step S802 is equal to or less than a predetermined threshold, it is determined that the level change constantly occurs in the mean value of the luminance signal.

In step S805, it is determined, for each measurement area, whether or not the level change hardly occurs in the mean value of the luminance signal. If it is determined that the level change hardly occurs (YES in step S805), then the process proceeds to step S808. If it is determined that the level change occurs but does not constantly occur (NO in step S805), then the process proceeds to step S806. Here, when the ratio of the maximum value and the minimum value obtained in step S802 is equal to or smaller than a predetermined value (i.e., close to <NUM>), it is determined that the level change hardly occurs in the mean value of the luminance signal.

In step S806, the measurement area is not specified. The measurement area such as a measurement area <NUM> shown in <FIG>, which is subjected to step S806, indicates an area to which any one of algorithms A and B cannot be appropriately applied.

In step S807, the measurement area such as a measurement area <NUM> shown in <FIG> in which the level change constantly occurs in the mean value of the luminance signal becomes subjected to step S807. Thus, this measurement area is specified as an area to which algorithm B (a means for determining it as abnormal when there is no change in the mean value of the luminance signal) is to be applied. At this time, since the longest time period during which there is no change in the mean value of the luminance signal is known for this measurement area, the predetermined time period used in step S504 of algorithm B may be automatically set by adding a predetermined margin to the longest time period. For example, if it can be known that there is an area in which there is no change in the mean value of the luminance signal for <NUM> minutes in maximum or less (that is, there is a movement within <NUM> minutes), the method for determining it as abnormal when there is no change in the mean value of the luminance signal is used to monitor this area. The graph <NUM> in <FIG> represents the above-described case.

In step S808, the measurement area such as a measurement area <NUM> shown in <FIG> in which the level change hardly occurs in the mean value of the luminance signal becomes subjected to step S808. Thus, this measurement area is specified as an area to which algorithm A (a means for determining it as abnormal when there is a change in the mean value of the luminance signal) is to be applied. At this time, since the minimum and maximum values of the mean value of the luminance signal are known for this measurement area, an upper limit and a lower limit of the set range used in step S404 of algorithm A may be automatically set by adding a predetermined margin to each of the minimum and maximum values. It is significant to set the maximum value since the abnormality due to, e.g., a deterioration of the image pickup unit may include a phenomenon such as white-black inversion in addition to a decrease in sensitivity.

<FIG> and <FIG> are a flowchart of algorithm D. Algorithm D is applied to determine the failure of the image sensor unit when there is no change in a video-image in which a moving object is removed after adjustments of an iris and a gain (exposure time and pixel gain) of the image sensor unit, which bring a change in the captured video-image.

In step S601, the measurement unit <NUM> acquires the latest digital image data of one picture that is outputted from the image sensor unit <NUM>.

Next, in step S602, similarly to step S402, an image with the moving object removed or masked is created from the acquired digital image data.

Next, in step S603, the moving-object-removed image in step S602 is compared with a previous moving-object-removed image (fixedly stored or previously updated) in the storage unit <NUM>, and if a difference therebetween does not exceed a threshold, then the process proceeds to step S601. If the difference therebetween exceeds the threshold, then the process proceeds to step S604. Here, the difference can be calculated by the sum of absolute differences. Moreover, the moving-object-removed image may be updated by, e.g., the weighted addition of two compared moving-object-removed images and stored in the storage unit <NUM>. However, when it is intended to detect the decrease in sensitivity due to the aging deterioration in step S603, the update should not be carried out.

Further, the processing of step S603 is not essential, and step S602 and subsequent step S604 may be carried out periodically by the schedule specified in advance.

In step S604, for example, the iris of the image sensor unit <NUM> is adjusted and changed. This adjustment may be performed through the lens control unit <NUM> either by autonomously sensing a change in the digital image data or based on instructions from the measurement unit <NUM> to the lens control unit <NUM>. The instructions from the measurement unit <NUM> may include an instruction to compensate the change in the video-image detected in step S603, a completely unrelated instruction (noise) and the like. Although it is not essential, the measurement unit <NUM> may be able to realize which direction a control amount has been changed with respect to which type of a lens unit.

Next, in step S605, after the adjustment of the image sensor unit <NUM> is carried out, a moving-object-removed image is newly created by performing the same processing as steps S601 and S602, and the current moving-object-removed image is compared with the one before the current moving-object-removed image (e.g., the moving-object-removed image created in step S602). Here, since the photographing conditions are changed, it is a normal when there is a change. When there is no change, the process proceeds to step S610. Further, the update of the moving-object-removed image is not carried out after the adjustment of the image sensor unit <NUM> is carried out.

In step S606, the current moving-object-removed image in step S602 is compared with a previous moving-object-removed image stored before the adjustment of the image sensor unit <NUM> in the storage unit <NUM>, and the process proceeds to step S601 when the difference therebetween does not exceed a predetermined threshold. Here, "the difference therebetween does not exceed a predetermined threshold" indicates that the control amount with respect to the image sensor unit returns to its original level or is fixed to correspond to changes in the photographing environment. The difference in step S606 may be a difference after a high pass filtering (HPF) or an edge detection instead of a difference of the pixel value itself (SAD), or may be a difference obtained by performing such filtering process on a differential image which is not subjected to the sum of absolute differences.

Next, in step S607, it is determined whether or not the adjustment of the image sensor unit has been performed a certain number of times or more. The process proceeds to step S604 when such adjustment is carried out less than the certain number of times. This provides a loop process that can exit to step S609 from step S606 in case where the sensor is normally operated. When such adjustment is carried out the certain number of times, the process proceeds to step S608. Although the control amount with respect to the image sensor unit <NUM> is changed for each adjustment, a random walk or an addition of a random walk to a change in a direction to reduce the difference with respect to the previous moving-object-removed image may be applied. Moreover, there is some delay after the control amount with respect to the image sensor unit <NUM> is changed until the image data based thereon is obtained. It is preferable that the time required to perform the certain number of adjustments is set to be sufficiently longer than the delay.

In step S608, when the image does not return to its original state after the change is recognized in step S603, it is determined that the angle of view is abnormal. Here, the abnormality of the angle of view includes external factors such as a blindfold, lens damage, stoppages of some of lighting devices and the like other than a sensor issue such as a change of a video-image capturing direction. In this case, it is highly possible that the sensor is not completely failed and is in operation to capture an image of something. However, a partial failure of the sensor (i.e., reading failure of some of the pixel values) can be included in the abnormality of the angle of view. Then, abnormality detection information such as a detection type indicating a type of the abnormality of the angle of view is generated. Further, if necessary, the abnormality detection information such as a detection area (i.e., a region having a large number of pixels having values different from those in the previous moving-object-removed image) may also be generated.

In step S609, it is determined that the sensor has no problem.

In step S610, it is determined whether or not the proceeding from S605 to step S610 has occurred continuously a certain number of times or more (or it is determined whether or not the adjustment of the image sensor unit (step S604) has been performed a certain number of times or more). The process proceeds to step S604 when it does not reach the certain number of times.

In step S611, since the image does not change at all even though the image sensor unit is adjusted after the change is recognized in step S603, it is determined that there is a failure of the sensor: Here, the failure of the sensor also includes an event that a video-image is suddenly frozen or blacked out in a large part of a frame and an event that a video-image is constantly and terribly fluctuated on a pixel-by-pixel basis. In such video-image, the removal of the moving object is always employed in a large part of the frame to thereby replace the previous removal of the moving object, the process proceeds to step S611. Then, abnormality detection information such as a detection type indicating the failure of the sensor is generated. Further, in step S611, it is determined that there is the failure of the sensor even when all of the lighting devices in the monitoring area are turned off due to a power failure or the like.

In step S612, the detection information (e.g., the failure detection information <NUM> and <NUM>) obtained in step S608, step S609 or step S611 is transmitted to the alarm management unit <NUM> and the video delivery unit <NUM>.

In step S613, alarms are aggregated in the server <NUM> which is capable of receiving the alarms from a plurality of the IP cameras <NUM> to be displayed on the display device <NUM>. For example, if it can be estimated that an external or a single cause generates the multiple alarms at substantially the same time, the multiple alarms can be aggregated into a single alarm. For example, if sensor-failure alarms are simultaneously generated from a plurality of cameras, it is highly possible that lighting devices are turned off or a power failure occurs. Even in case where abnormalities in the angle of view are simultaneously generated, it can be estimated that environmental changes such as an earthquake or illuminance changes in a wide area over a plurality of the monitoring areas have occurred. If it has been experienced and studied that such events are generated frequently or at a fixed time, such alarms can be deleted instead of being displayed. In addition, in a single camera, if alarms are generated from both of the measurement areas subjected to algorithms A and B at substantially the same time, it can be determined that it is highly possible that at least one of an abnormality in the angle of view and a sensor failure is generated in the corresponding single camera.

According to the embodiments described above, the monitoring system of the present invention is capable of detecting video-image abnormalities of all of the camera devices even in large-scale monitoring systems.

Claim 1:
A camera device (<NUM>), comprising:
an image sensor unit (<NUM>); and a measurement unit (<NUM>) configured to
divide image data (<NUM>) acquired by the image sensor unit (<NUM>) into a plurality of areas;
measure (S801) a change in the mean value of the luminance signal in each of the areas for a predetermined time period and
store (S802, S803) a longest time period during which a state where there is no change in the mean value of the luminance signal is maintained, and a minimum value and a maximum value of the mean value of the luminance signal for the predetermined time period after the predetermined time period has lapsed,
wherein when the longest time period is equal to or less than a preset threshold, the measurement unit (<NUM>) determines that the change constantly occurs (S804 YES) and specifies the corresponding area as a measurement area for determining that a video-image abnormality has occurred when there is no change in the mean value of the luminance signal in the measurement area (S807), and when the longest time period is greater than the preset threshold (S804 NO), the measurement unit (<NUM>) determines whether a ratio of the maximum value and the minimum value is equal to or smaller than a preset value (S805), and if it is determined that the ratio is equal to or smaller than the preset value, the measurement unit (<NUM>) determines that the change hardly occurs (S805 YES) and specifies the corresponding area as a measurement area for determining that a video-image abnormality has occurred when there is a change in the mean value of the luminance signal in the measurement area (S808), and
wherein the state where there is no change in the mean value of the luminance signal indicates that the change is less than <NUM>% of a rating value.