Information processing device, information processing method, and program

Abstract

To make it possible to identify images with reduced blur from multiple images without spending time on processing.SOLUTION: An information processing device acquires inertial information from images acquired at the time of image capture and added with the inertial information and identifies images in which the amount of blur at the time of image capture is less than a threshold value on the basis of the inertial information.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an information processing technology for identifying an image from a plurality of images.

Background Art

[0002] In recent years, cameras capable of recording high-resolution videos such as so-called 4K and 8K have begun to spread. Since the images of each frame constituting videos such as 4K and 8K have sufficient resolution, they can also be used as still images. However, among the images of each frame of the video, there are also images in which the subject of the imaging target is blurred. The blurring of the image of the subject of the imaging target is caused by blur due to the movement of the subject, so-called camera shake or camera movement due to camera work, and out-of-focus of the subject. On the other hand, Patent Document 1 discloses a technique that analyzes the blur of an image for each frame of a video and enables selection of a frame with less blur based on the analysis result.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, performing a process of analyzing the blur for all the images of all the frames takes a long time for the process.

[0005] Therefore, an object of the present invention is to be able to identify an image with less blur from a plurality of images without taking time for the process.

Means for Solving the Problems

[0006] The information processing apparatus of the present invention includes: an information acquisition unit that acquires the inertial information from an image to which the inertial information acquired at the time of imaging is added; and a specifying unit that specifies, based on the inertial information, an image in which the amount of blur at the time of imaging is less than a threshold value.

Advantages of the Invention

[0007] According to the present invention, it is possible to specify an image with less blur from a plurality of images without taking time for processing.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Embodiments for Carrying Out the Invention

[0009] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The following embodiments do not limit the present invention, and not all combinations of the features described in the present embodiments are essential for the solution means of the present invention. The configuration of the embodiments can be appropriately modified or changed according to the specifications of the apparatus to which the present invention is applied and various conditions (usage conditions, usage environments, etc.). Also, a part of each of the following embodiments may be appropriately combined to form a configuration. In the following embodiments, the same reference numerals are given to the same configurations and steps for description.

[0010] <System Configuration Example> FIG. 1(A) is a diagram showing an example of a system configuration including an information processing apparatus according to an embodiment. As shown in FIG. 1(A), as an example, the system of this embodiment is configured to include a camera 101, an information processing apparatus 102, and a display 103. Note that these configurations are just examples, and other configurations such as a communication device for connecting to a printer, a network, etc. may also be included.

[0011] The camera 101 is an imaging device capable of continuously capturing high-resolution videos such as 4K or 8K, and high-resolution still images. The camera 101 is also equipped with an inertial sensor, and has a function of adding the inertial information detected by the inertial sensor to an image by including it in metadata. The inertial sensor included in the camera 101 includes a gyro sensor, an acceleration sensor, etc., and also includes a geomagnetic sensor for detecting the movement of the camera based on geomagnetism. In this embodiment, when the camera 101 captures a video, for example, the inertial information detected by the inertial sensor at the time of capturing each frame of the video is added to the image for each frame. Also, for example, when capturing a still image, the camera 101 adds the inertial information detected by the inertial sensor at the time of capturing the still image to each still image. Hereinafter, an image with inertial information added to the metadata will be referred to as an image with metadata. Then, the image with metadata is sent to the information processing apparatus 102. Note that the metadata includes, in addition to the inertial information, information related to imaging in the camera 101, such as information related to imaging such as shutter speed, exposure, and other setting values.

[0012] The image with metadata may be sent from the camera 101 to the information processing apparatus 102 via a wireless or wired communication line, or may be recorded on a memory card (not shown) by the camera 101 and then read by the information processing apparatus 102 from the memory card. Note that although the camera 101 can also correct camera shake, etc. based on the inertial information detected by the inertial sensor, in this embodiment, it is assumed that an image in which no blur correction is performed by the camera 101 or an image with little blur correction is sent to the information processing apparatus 102.

[0013] The information processing apparatus 102 is, for example, a PC (Personal Computer). In the information processing apparatus 102, the CPU 115 controls the operation of the entire camera in cooperation with other components based on computer programs such as an OS (Operating System) and application programs. In the example of FIG. 1(A), one example of the CPU is given, but it is not limited to this, and a configuration in which there are a plurality of CPUs may be used. In that case, each process can be operated in parallel by multi-thread processing.

[0014] The display 103 is a display device that displays a UI (User Interface) screen, an image of an information processing result, and the like. The GPU 113 is a graphics processor that performs the computational processing required when displaying an image on the display 103 and outputs the display image to the display 103. The UI (User Interface) screen, the image of the information processing result, etc. are sent to the display 103 via the GPU 113 and displayed. Also, the GPU 113 can encode and decode images in real time. In the present embodiment, when the image data is encoded, the GPU 113 decodes the encoded image data and converts it into an internal image format.

[0015] The RAM (Random Access Memory) 112 temporarily stores data during processing in the CPU 115 and the GPU 113. The storage 117 is a large-capacity storage such as an SSD (Solid State Drive) or an HDD (Hard Disk Drive), and records data and programs.

[0016] The user I / F (Interface) 116 is an interface to which a touch panel, a mouse, a keyboard, etc. are integrally connected. The external input / output I / F 114 is an interface for connecting a network and a memory card to input and output data. The bus 111 controls the flow of data within the PC.

[0017] Computer programs such as operating systems, application programs, and the information processing program according to the present embodiment, as well as various data, are recorded in the storage 117, and the CPU 115 executes the program expanded from the storage 117 to the RAM 112. Input / output of programs and data is performed between the CPU 115, the RAM 112, and the storage 117 via the bus 111. In addition, image data and the like after being processed by the CPU 115 or the GPU 113 are recorded on a recording medium such as a memory card (not shown) via the storage 117 or the external input / output I / F 114, and can be shared by other devices or applications.

[0018] <Information Processing According to the First Embodiment> As described above, since the images acquired by the camera 101 are high-resolution videos such as 4K or 8K, or high-resolution still images, the images selected from among the images of each frame of these videos or from among a plurality of still images are high-resolution images. The information processing apparatus 102 according to the present embodiment also has a function of selecting an image from among the images of each frame of the video acquired from the camera 101 or from among a plurality of still images.

[0019] However, among the images of each frame of the video acquired by the camera 101 and a plurality of still images, there are also images in which the subject to be imaged is blurred. As described above, the blurring of the image of the subject to be imaged is caused by blurring due to the movement of the subject or the like, blurring due to the movement of the camera due to camera shake or camera work, and out-of-focus of the subject. Note that the blurring caused by the movement of the subject or the movement of the camera is also called motion blur. Motion blur is caused by the accumulation of blurring due to the movement of the subject or the movement of the camera within the exposure time, and thus will be referred to as "accumulated blur" in the following description. Also, generally, when imaging a video, in order to suppress so-called jerkiness, the images of the frames are often captured at a slow shutter speed. For example, when the frame rate is 30 fps (frames per second), the images of the frames are often captured at a shutter speed of 1 / 30 second. However, in the images of the frames captured at a slow shutter speed, accumulated blur is likely to occur in which the blurring of the subject to be photographed, camera shake, and blurring having directivity in the direction of the movement of the camera due to camera work accumulate within the exposure time.

[0020] When selecting an image from the images of each frame of the video acquired from the camera 101 or a plurality of still images, the information processing apparatus 102 according to the first embodiment can identify an image with less accumulated blur by using the inertial information added to the image by the camera 101. That is, the information processing apparatus 102 according to the present embodiment has a function of executing information processing for extracting metadata from the acquired plurality of images with metadata and identifying an image with less accumulated blur based on the inertial information included in the metadata for each of those images.

[0021] <Functional Configuration and Information Processing of Information Processing Apparatus> FIG. 1(B) is a diagram showing the main functional configuration of the information processing apparatus 102 according to the present embodiment. Each functional configuration shown in FIG. 1(B) is configured by the CPU 115 of the information processing apparatus 102 executing the information processing program according to the present embodiment. Hereinafter, a case where a video is acquired from the camera 101 will be described as an example. As shown in FIG. 1(B), the information processing apparatus 102 includes functional configurations of an image acquisition unit 121, an inertial information acquisition unit 122, a blur amount acquisition unit 123, and a specifying unit 130. The image acquisition unit 121 acquires a metadata - attached image in which metadata including inertial information is added for each image of a frame of a moving image. The inertial information acquisition unit 122 acquires inertial information from the metadata - attached image acquired by the image acquisition unit 121. The blur amount acquisition unit 123 acquires information representing the amount of accumulated blur based on the inertial information acquired by the inertial information acquisition unit 122. The specifying unit 130 specifies an image in which the amount of accumulated blur is less than a predetermined blur threshold among each frame of the moving image, and generates identification information for identifying the specified image.

[0022] The specifying unit 130 includes a determination unit 124, a listing unit 125, and a list generation unit 126. The determination unit 124 determines, for each frame of the moving image, whether the amount of accumulated blur of the image is less than the blur threshold. The listing unit 125 specifies frames determined by the determination unit 124 to have an accumulated - blur amount less than the blur threshold from among each frame of the moving image, and generates identification information for identifying the specified frames. In the case of this embodiment, the listing unit 125 generates a list of each frame specified as having an accumulated - blur amount less than the blur threshold as the identification information. The list generation unit 126 generates a list of images having an accumulated - blur amount less than the threshold based on the list generated by the listing unit 125.

[0023] FIG. 2 is a flowchart showing a rough flow of information processing until the information processing apparatus 102 according to the present embodiment identifies and displays a list of images of frames in which the amount of accumulated blur in a video is less than a threshold value. In the following description, accumulated blur with an amount of blur less than the blur threshold value will be referred to as low accumulated blur. The information processing of the flowchart shown in FIG. 2 is executed by each functional unit shown in FIG. 1(B) configured by the CPU 115 of the information processing apparatus 102. Unless otherwise specified, each processing step (processing process) of each flowchart described hereinafter is executed in the order indicated by the arrow from "start" to "end". Also, the frame rate of the video is assumed to be 30 fps. In each of the following flowcharts, it is assumed that the reference symbol S represents a processing step (processing process).

[0024] First, as the process of S201, the image acquisition unit 121 acquires an image with metadata via the external input / output I / F 114, and the inertial information acquisition unit 122 acquires inertial information included in the metadata from the image with metadata. In the case of the present embodiment, the inertial information includes information detected by a gyro sensor included in the inertial sensor provided in the camera 101, information detected by an acceleration sensor, and information detected by a geomagnetic sensor.

[0025] Next, as the process of S202, the blur amount acquisition unit 123 acquires information indicating the amount of accumulated blur of the image for each frame of the video based on the inertial information acquired in S201. Further, in S202, the determination unit 124 of the specific unit 130 determines whether the amount of accumulation blur of the image for each frame of the video is less than a predetermined blur threshold. Then, the listing unit 125 identifies each frame of low accumulation blur whose amount of accumulation blur is determined to be less than the blur threshold from among the frames of the video, and generates identification information for identifying those identified frames. In the case of this embodiment, the listing unit 125 generates an image list in which the identification information for identifying the identified frames is listed. In the case of this embodiment, the image list is generated as a list consisting of the frame numbers of the frames that are determined to be low accumulation blur, with the frame numbers representing the frames constituting the video as elements. Note that the data structure of the image list is not limited to the list format, and may be, for example, a variable-length array or the like. Details of the processing from the acquisition of the amount of blur to the generation of the image list in S202 will be described later.

[0026] Next, as the process of S203, the list generation unit 126 generates a list of thumbnail images based on the image list of the frame numbers of the low accumulation blur generated in S202. Then, the data of this list is sent to the GPU 113, and the GPU 113 generates display data for the list and displays it on the screen of the display 103. Note that the thumbnail images may be generated by the GPU 113.

[0027] FIG. 3 is a detailed flowchart of the processing from the acquisition of the amount of blur to the generation of the image list in S202 of FIG. 2. First, as the process of S301, the blur amount acquisition unit 123 initializes i, which is used as a value indicating the frame number (initialized to i = 0). Next, as the process of S302, the blur amount acquisition unit 123 acquires inertial information from the metadata added to the image of the frame corresponding to the frame number i.

[0028] Next, as the process of S303, the shake amount acquisition unit 123 calculates the shake amount m of the accumulated shake from the inertial information corresponding to the frame number i. In the present embodiment, it is assumed that the shake amount is calculated using the angular velocity information which is the detection information of the gyro sensor included in the inertial information. In the case of the present embodiment, the angular velocity information of the gyro sensor is composed of information on three axes per frame, that is, for example, three angular velocity information in the pan direction, tilt direction, and roll direction. Let the angular velocity component in the pan direction be p, the angular velocity component in the tilt direction be t, and the angular velocity component in the roll direction be r. Then, the shake amount m is expressed by the following formula (1). Note that the unit is degrees / frame. That is, it is the data of 30 Hz of the inertial information.

[0029] m = √(p 2 + t 2 + r 2 ) Formula (1)

[0030] Next, as the process of S304, the determination unit 124 acquires the shake threshold Th. In the case of the present embodiment, the determination unit 124 calculates the shake threshold Th based on the shutter speed information included in the metadata added to the frame of the frame number i. Let the shutter speed be s. Then, the shake threshold Th is calculated by the following formula (2).

[0031] Th = k × s Formula (2)

[0032] For example, in the case of obtaining a shake amount of 0.05 degrees or less per frame for a video of 30 fps, the coefficient k in formula (2) is calculated as k = 0.05 × 30 = 1.5. Next, as the process of S305, the determination unit 124 determines whether the shake amount is less than the shake threshold (m > Th). Then, when the shake amount m is less than the shake threshold Th, the determination unit 124 sets a bool variable indicating whether the shake amount of the accumulated shake is small, i.e., low accumulated shake, to true and proceeds to the process of S306. On the other hand, when the shake amount m is greater than or equal to the shake threshold Th, the determination unit 124 sets a bool variable indicating whether the accumulated shake is low accumulated shake to false and proceeds to the process of S307.

[0033] When proceeding to the process of S306, the listing unit 125 adds the frame number i to the image list. On the other hand, when proceeding to the process of S307, the listing unit 125 determines whether the processing of all frames has been completed. If the processing of all frames has been completed, the process of the flowchart in FIG. 2 ends. If not, the process proceeds to S308.

[0034] When proceeding to the process of S308, the listing unit 125 sets the frame number i as i = i + 1, increments i, and then returns the process to S302. Thereafter, the processes from S302 to S308 are repeatedly executed until it is determined that the processing ends in S307.

[0035] Here, for example, the number of frames of a video of one sequence ranges from several thousand to several tens of thousands. In the case of this embodiment, by performing the process of identifying frames with low accumulation blur based on the inertial information acquired by the inertial sensor, it is not necessary to perform the process of analyzing the image, and frames with less accumulation blur can be identified from the frames of the video in a short time. And according to this embodiment, by displaying a list of the images of the identified frames as thumbnail images, the user can select the images of the frames with less blur that the user likes. Note that the images of the selected frames may be converted and saved in, for example, the still image format JPEG, or printed.

[0036] In this embodiment, as an example of the angular velocity component of the inertial information, a set of three-axis data of pan, tilt, and roll for each frame of 30 fps is given, but it is not limited thereto. For example, high-frequency data such as 300 Hz may be used. By integrating the 300 Hz data, angular velocity data at 30 Hz can be created.

[0037] In this embodiment, an example was given in which the blur threshold for determining whether the accumulated blur is low accumulated blur is calculated based on the shutter speed. However, the present invention is not limited to this, and a predetermined fixed value may be used. For example, the blur amounts of all the frames included in one video sequence are calculated, and when the blur amounts for each of these frames are arranged in ascending order, the blur amount of the frame corresponding to the upper predetermined percentage with respect to the total number of frames may be set as the blur threshold. Alternatively, when the blur amounts for each of these frames are arranged in ascending order, the blur amount of the frame corresponding to the upper predetermined number with respect to the total number of frames may be set as the blur threshold. Note that examples of the frames corresponding to the upper predetermined percentage in ascending order of the blur amount include, for example, the frames corresponding to the upper 0.1% with respect to the total number of frames. In the case of these examples, setting the blur amount of the frame corresponding to the upper 0.1% in ascending order of the blur amount, or the blur amount of the frame corresponding to the upper predetermined number, as the blur threshold is synonymous with executing the processing of the flowchart in FIG. 3. Further, the blur amount of the frame corresponding to the upper 0.1% in ascending order of the blur amount, or the blur amount of the frame corresponding to the upper predetermined number, may be recorded as metadata as a threshold value.

[0038] In this embodiment, when calculating the blur amount m, all three components in the pan direction, tilt direction, and roll direction are taken into account. However, the present invention is not limited to this. In general camera work, there is movement in the pan direction and tilt direction, while camera work in the roll direction is often not performed. Therefore, the blur amount m may be calculated only from the angular velocity component p in the pan direction and the angular velocity component t in the tilt direction as shown in the following formula (3).

[0039] m = √(p 2 + t 2 ) Formula (3)

[0040] In this embodiment, an example was described in which the blur amount is calculated using only the detection information of the gyro sensor among the detection information of the gyro sensor, the acceleration sensor, and the geomagnetic sensor included in the inertial information. However, the present invention is not limited to this. The acceleration information, which is the detection information of the acceleration sensor, and the geomagnetic information, which is the detection information of the geomagnetic sensor, may be used in combination to calculate the blur amount. In other words, the inertial information does not necessarily have to include these three pieces of information, and for example, it may include only the detection information by the gyro sensor. Further, the blur amount information may be obtained as one of the inertial information in the camera 101, and the blur amount information may be added to the image as metadata by including it in the inertial information. In this case, in the blur amount acquisition unit 123 of FIG. 1(B), instead of calculating the blur amount in S303, the blur amount embedded in the inertial information will be acquired.

[0041] <Second Embodiment> In the first embodiment described above, an example was described in which an image of a frame with less accumulated blur is specified from among the frames of the moving image. On the other hand, in the second embodiment, an image is specified from the moving image in consideration of not only the accumulated blur but also blurring caused by out-of-focus of the subject. In the case of the second embodiment, an example will be described in which, among the frames in which the blur amount of the accumulated blur is less than the blur threshold, frames in which the amount of blurring due to out-of-focus or the like to the subject is less than a predetermined blur threshold (hereinafter referred to as less-blurred frames) are further specified to generate a list. In the second embodiment, since the system configuration and the configuration of the information processing apparatus 102 and the like are the same as those in FIG. 1, the illustration and description thereof are omitted. In the following description, the configuration and processing different from those of the first embodiment will be mainly described.

[0042] FIG. 4 is a flowchart showing a rough flow of information processing for specifying further less-blurred frames among the frames of the moving image in which the blur amount of the accumulated blur is less than the blur threshold in the information processing apparatus 102 of the second embodiment and enabling a list display. Note that the information processing of the flowchart shown in FIG. 3 is also executed in each functional unit shown in FIG. 1(B) of the CPU 115 of the information processing apparatus 102 in the same manner as in the case of the first embodiment.

[0043] First, as the process of S401, similar to the first embodiment described above, the image acquisition unit 121 acquires an image with metadata, and the inertial information acquisition unit 122 acquires the inertial information included in the metadata from the image with metadata.

[0044] Next, as the process of S402, similar to the first embodiment described above, the blur amount acquisition unit 123 acquires information indicating the blur amount of the accumulated blur of the image for each frame of the video based on the inertial information acquired in S401. Also in S402, the determination unit 124 determines whether the blur amount of the accumulated blur of the image for each frame of the video is less than a predetermined blur threshold. Then, similar to the first embodiment, the listing unit 125 identifies each frame of low accumulated blur for which the blur amount of the accumulated blur is determined to be less than the blur threshold from among the respective frames of the video, and generates an image list with identification information for identifying those identified frames. Here, in the case of the second embodiment, the determination unit 124 analyzes the image of the frame of low accumulated deviation to acquire the amount of blur, identifies the frames with less blur for which the amount of blur is less than a predetermined blur threshold, and adds a less blur flag to those frames with less blur. Details of the process in S402 will be described later.

[0045] Next, as the process of S403, the list generation unit 126 generates a list of thumbnail images based on the image list generated in S402. Also in the case of the second embodiment, the list generation unit 126 can also generate a list of frames to which the less blur flag is added in S402. Then, the data of these lists is sent to the GPU 113, and the GPU 113 generates display data of the list and displays it on the screen of the display 103. That is, in the case of the second embodiment, not only a list of thumbnail images of the frames of low accumulated deviation but also a list of thumbnail images of the frames with less blur can be displayed on the screen of the display 103.

[0046] FIG. 5 is a flowchart of the process in S402 of FIG. 3. In the flowchart of FIG. 5, the processes of S301 to S305, S307, and S308 are the same as the corresponding process steps in FIG. 3 described above, so their descriptions are omitted. In the case of the flowchart of FIG. 5, in S305, if the blur amount m is less than the blur threshold Th, the process proceeds to S501.

[0047] When the process proceeds to S501, the listing unit 125 adds the frame number i with the blur amount m less than the blur threshold Th to the image list, in the same manner as in the case of the first embodiment described above. Further, in the case of the second embodiment, in S501, the determination unit 124 analyzes the image information of the frame determined that the blur amount m is less than the blur threshold Th in S305 to obtain the blur amount of the image. Note that the process for obtaining the blur amount by analyzing the image information of the frame in S501 is not particularly limited, and any of various known methods as disclosed in Patent Document 1 may be used.

[0048] Next, as the process of S502, the determination unit 124 determines whether the blur amount obtained by analyzing the image information performed in S501 is less than the blur threshold. If it is determined that the blur amount is less than the blur threshold, the process proceeds to S503. If it is determined that the blur amount is equal to or greater than the blur threshold, the process proceeds to S307.

[0049] When it is determined that the blur amount is less than the blur threshold and the process proceeds to the process of S503, the determination unit 124 adds a little blur flag to the frame determined that the blur amount is less than the blur threshold in the image list described above, that is, the little blur frame. In the case of the second embodiment, for the little blur frame with the blur amount less than the blur threshold, a bool variable indicating whether the accumulated blur is low accumulated blur is set to true. In other words, for the little blur frame determined that the blur amount is equal to or greater than the blur threshold, it is set to false when the accumulated blur is large. However, these pieces of information are managed by the listing unit 125 in the memory and are referred to during the process of S403 in FIG. 4 by the list generation unit 126.

[0050] That is, in the case of the second embodiment, the list generation unit 126 can not only display a list of thumbnail images of low-accumulation blurs, but also display a list of thumbnail images of frames with little blur, such as out-of-focus blur to the subject, by referring to the little-blur flag.

[0051] In the second embodiment, as described above, the information processing apparatus 102 does not analyze the images of all frames of the video, but analyzes only the image information of the frames identified as low-accumulation blurs based on the inertial information, and identifies the little-blur frames based on the analysis results. Therefore, according to the second embodiment, compared with the case of analyzing the images of all frames of the video, it is possible to identify and display the little-blur frames from the frames of the video in a very short processing time.

[0052] In the second embodiment, in S501, it was assumed that the frame number i of the frame in which the blur amount m is less than the blur threshold Th is added to the image list in the same manner as in the first embodiment, but this process may not be performed. In this case, the determination unit 124 analyzes the image information of the frames in which the blur amount m is less than the blur threshold Th in S501, and adds a little-blur flag to the little-blur frames determined to have a blur amount less than the blur threshold in the next S502 in S503. Also in this example, in S503, the listing unit 125 adds the frame number i of the little-blur frame to which the little-blur flag is added to the image list. That is, in this case, the image list becomes a list that can identify the little-blur frames. Thereby, it becomes possible to display only a list of thumbnail images of the little-blur frames.

[0053] Also, in the second embodiment, an example was given in which the process of analyzing image information and the process of determining whether the amount of blur is small are performed. However, if only the little blur flag is generated, the image analysis process and the blur amount determination process can be omitted. When these analysis process and determination process are not performed, the little blur flag becomes a flag indicating that the motion blur (i.e., accumulated blur) is small, similar to the case of the first embodiment. In other words, by adding the process steps of image analysis and determination to the flowchart of FIG. 3 in the first embodiment, a list of images with little blur can be generated.

[0054] Also, in the first and second embodiments, an example was described in which the CPU 115 executes an information processing program to perform the processing of each processing step. However, the present invention is not limited thereto, and dedicated hardware for performing each process may be provided as the device configuration, and the processing may be performed in each hardware. Also, in the second embodiment, it was described that the CPU 115 (listing unit 125) manages the little blur flag on the memory. However, the little blur flag itself may be added as image metadata and recorded on a recording medium such as a memory card. That is, if the little blur flag is added to the image metadata, for example, when a video is read out from the memory card for the second time, it is possible to identify the image of the little blur frame without calculating the amount of blur and performing image analysis again. Also, the calculated amount of blur for each frame and the threshold value may be recorded on, for example, a memory card. In this case, if the determination of the amount of blur is performed using the threshold value recorded on the memory card, it is possible to obtain the same result as determining whether there is blur using a flag.

[0055] In the first and second embodiments described above, an example was given in which the information processing apparatus 102 acquires inertial information included in metadata and performs the information processing described above. However, the present invention is not limited to this, and the same information processing as described above may be performed in the camera 101. In this case, separately from the inertial information, a flag indicating low accumulation blur or a little blur flag may be added as frame-by-frame metadata and recorded on the memory card. As a result, the information processing apparatus 102 can refer to the flag indicating low accumulation blur or the little blur flag added as metadata, identify frames with less accumulation blur or blur, and display them in a list.

[0056] The present invention can also be realized by supplying a program that realizes one or more functions of the above-described embodiments to a system or apparatus via a network or a storage medium, and having one or more processors in a computer of the system or apparatus read and execute the program. Further, it can also be realized by a circuit (for example, ASIC) that realizes one or more functions. The above-described embodiments are merely examples of specific embodiments for carrying out the present invention, and the technical scope of the present invention should not be construed as being limited thereby. That is, the present invention can be implemented in various forms without departing from its technical idea or its main features.

[0057] The disclosure of each embodiment includes the following configurations, methods, and programs. (Configuration 1) Information acquisition means for acquiring the inertial information from an image to which the inertial information acquired at the time of imaging is added; Specification means for specifying an image in which the amount of blur at the time of imaging is less than a threshold value based on the inertial information; An information processing apparatus, characterized by comprising: (Configuration 2) Having analysis means for analyzing the amount of blur of an image, The analysis means performs the analysis on an image in which the amount of blur is less than the threshold value, The information processing apparatus according to Configuration 1, wherein the specifying means specifies an image in which the amount of blur analyzed by the analyzing means is less than a blur threshold value. (Configuration 3) It has an analysis means for analyzing the amount of blur in an image, The analysis means performs the analysis on an image in which the amount of blur is less than the threshold value, The specifying means is the information processing apparatus according to Configuration 1, characterized by specifying an image in which the amount of blur is less than the threshold value and an image in which the amount of blur analyzed by the analysis means is less than the blur threshold value. (Configuration 4) The specifying means is the information processing apparatus according to any one of Configurations 1 to 3, which generates identification information for identifying the specified image. (Configuration 5) The specifying means is the information processing apparatus according to Configuration 4, characterized by generating a list of the specified images using the identification information. (Configuration 6) The specifying means is the information processing apparatus according to Configuration 5, characterized by generating a list of images based on the list. (Configuration 7) The specifying means is the information processing apparatus according to any one of Configurations 1 to 6, characterized by setting the threshold value based on the shutter speed at the time of imaging. (Configuration 8) The specifying means is the information processing apparatus according to any one of Configurations 1 to 6, characterized by setting, as the threshold value, the amount of blur of an image corresponding to a predetermined ratio on the upper side when arranging the amounts of blur of a plurality of images to which the inertia information is added in ascending order, or the amount of blur of an image corresponding to a predetermined number on the upper side when arranging them in ascending order. (Configuration 9) The information processing apparatus according to Configuration 2 or 3, characterized by having means for adding a predetermined flag to an image in which the amount of blur is less than the blur threshold value and recording it on a recording medium. (Configuration 10) The inertia information is the information detected by an inertia sensor provided in an imaging device that captures the image, and the information processing apparatus according to any one of Configurations 1 to 9. (Configuration 11) The information processing apparatus according to any one of Configurations 1 to 10, wherein the image is an image of each frame constituting a video or an image of a plurality of continuously captured still images. (Method 1) An information acquisition step of acquiring the inertial information from an image to which the inertial information acquired at the time of imaging is added; A specifying step of specifying an image in which the amount of blur at the time of imaging is less than a threshold value based on the inertial information; An information processing method, characterized by comprising: (Program 1) A program for causing a computer to function as the information processing apparatus according to any one of Configurations 1 to 11.

Explanation of Signs

[0058] 101: Camera, 102: PC, 103: Display, 111: Bus, 112: RAM, 113: Graphics Processor, 114: External Data Input / Output I / F, 115: CPU, 116: User I / F, 117: Storage

Claims

1. Information acquisition means for acquiring the inertial information from an image with the inertial information added during imaging; Specification means for specifying an image in which the amount of blur during the imaging is less than a threshold value based on the inertial information; An information processing apparatus comprising the same.

2. Having analysis means for analyzing the amount of blur of an image, The analysis means performs the analysis on an image in which the amount of blur is less than the threshold value, The information processing apparatus according to claim 1, wherein the specifying means specifies an image in which the amount of blur analyzed by the analysis means is less than a blur threshold value.

3. Having analysis means for analyzing the amount of blur of an image, The analysis means performs the analysis on an image in which the amount of blur is less than the threshold value, The information processing apparatus according to claim 1, wherein the specifying means specifies an image in which the amount of blur is less than the threshold value and an image in which the amount of blur analyzed by the analysis means is less than a blur threshold value.

4. The information processing apparatus according to any one of claims 1 to 3, wherein the specifying means generates identification information for identifying the specified image.

5. The information processing apparatus according to claim 4, wherein the specifying means generates a list of the specified images using the identification information.

6. The information processing apparatus according to claim 5, wherein the specifying means generates a list of images based on the list.

7. The information processing apparatus according to any one of claims 1 to 3, wherein the specifying means sets the threshold value based on the shutter speed during the imaging.

8. The information processing apparatus according to any one of claims 1 to 3, wherein the specifying means sets, as the threshold value, the amount of blur of an image corresponding to a predetermined ratio on the upper side when arranging the amounts of blur of a plurality of images with the inertial information added in ascending order, or the amount of blur of an image corresponding to a predetermined number on the upper side when arranging them in ascending order.

9. The information processing apparatus according to claim 2 or 3, further comprising means for adding a predetermined flag to an image in which the amount of blur is less than a blur threshold value and recording the image on a recording medium.

10. The information processing apparatus according to any one of claims 1 to 3, wherein the inertial information is information detected by an inertial sensor provided in an imaging device that captures the image.

11. The information processing apparatus according to any one of claims 1 to 3, wherein the image is an image of each frame constituting a video or an image of a plurality of continuously captured still images.

12. An information acquisition step of acquiring the inertial information from an image to which the inertial information acquired at the time of imaging is added; A specifying step of specifying an image in which a blur amount at the time of imaging is less than a threshold value based on the inertial information; An information processing method characterized by comprising:

13. A computer, An information acquisition means for acquiring the inertial information from an image to which the inertial information acquired at the time of imaging is added; A specifying means for specifying an image in which a blur amount at the time of imaging is less than a threshold value based on the inertial information; A program for causing the computer to function as an information processing apparatus having the above.

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