An apparatus and a method to estimate a degree of user fatigue to video content are disclosed including a feature value calculating unit and a fatigue degree calculating unit. The feature value calculating unit is configured to calculate feature values corresponding to image and sound features in a video content. The fatigue degree calculating unit is configured to calculate a degree of user fatigue to the video content by applying the feature values to a fatigue degree estimation model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus, comprising: a feature value calculating unit configured to calculate feature values corresponding to image and sound features in a video content; and a fatigue degree calculating unit configured to calculate a degree of user fatigue to the video content by applying the feature values to a fatigue degree estimation model. 2. The apparatus of claim 1, wherein the feature value calculating unit further comprises: an image feature value calculating unit configured to calculate an image feature value corresponding to an image feature in the video content; a sound feature value calculating unit configure to calculate a sound feature value corresponding to sound feature in the video content; and an emotion feature value calculating unit configured to calculate an emotion feature value by applying at least one of the image feature value and the sound feature value to an emotion recognition model. 3. The apparatus of claim 2, wherein the emotion feature value comprises an arousal level and a valence level. 4. The apparatus of claim 1, wherein the fatigue degree estimation model comprises: a first parameter that represents a correlation between the image feature value and a bio-signal change of a user; and a second parameter that represents a correlation between the bio-signal change and a fatigue degree. 5. The apparatus of claim 2, wherein the fatigue degree calculating unit is further configured to calculate an auditory fatigue degree based on the sound feature value, a visual fatigue degree based on the image feature value, and a mental fatigue degree based on the emotion feature value. 6. The apparatus of claim 5, wherein the fatigue degree calculating unit is further configured to calculate an overall fatigue degree based on the auditory fatigue degree, the visual fatigue degree, and the mental fatigue degree. 7. The apparatus of claim 1, wherein the fatigue degree calculating unit is further configured to calculate an accumulative fatigue degree according to a lapse of viewing time, while a user is watching the video content. 8. The apparatus of claim 1, further comprising: a fatigue degree information generating unit configured to generate fatigue degree information to display the fatigue degree to a user. 9. A method, comprising: calculating feature values corresponding to image and sound features in a video content; and calculating a degree of user fatigue to the video content by applying the feature values to a fatigue degree estimation model. 10. The method of claim 9, wherein the calculating of the feature values comprises: calculating an image feature value corresponding to an image feature in the video content; calculating a sound feature value corresponding to a sound feature in the video content; and calculating an emotion feature value by applying at least one of the image feature value and the sound feature value to an emotion recognition model. 11. The method of claim 10, wherein the emotion feature value comprises an arousal level and a valence level. 12. The method of claim 9, wherein the fatigue degree estimation model comprises: a first parameter that represents a correlation between the image feature value and a bio-signal change of a user; and a second parameter that represents a correlation between the bio-signal change and a fatigue degree. 13. The method of claim 10, wherein the calculating of the fatigue degree comprises calculating an auditory fatigue degree based on the sound feature value, a visual fatigue degree based on the image feature value, and a mental fatigue degree based on the emotion feature value. 14. The method of claim 13, wherein the calculating of the fatigue degree comprises calculating an overall fatigue degree based on the auditory fatigue degree, the visual fatigue degree, and the mental fatigue degree. 15. The method of claim 9, wherein the calculating of the fatigue degree comprises calculating an accumulative fatigue degree according to a lapse of viewing time, while a user is watching the video content. 16. The method of claim 9, further comprising: generating fatigue degree information to display the fatigue degree to a user. 17. An apparatus, comprising: a feature value calculating unit configured to calculate image feature value, sound feature value, and emotion feature value associated with a video content; and a fatigue degree calculating unit configured to calculate a degree of user fatigue to video content by correlating bio-signal changes of a user to the image feature value, sound feature value, and emotion feature value. 18. The apparatus of claim 17, further comprising: a fatigue degree information generating unit configured to generate fatigue degree information to display the calculated degree of user fatigue. 19. The apparatus of claim 17, wherein the fatigue degree calculating unit is further configured to calculate the degree of user fatigue to video content from a mean value based on a degree of visual fatigue, a degree of auditory fatigue, and a degree of mental fatigue, each degree based on the image feature value, the sound feature value, and the emotion feature value, respectively. 20. The apparatus of claim 17, wherein the fatigue degree calculating unit is further configured to calculate the degree of user fatigue to video content by multiplying each of a degree of visual fatigue, a degree of auditory fatigue, and a degree of mental fatigue by a weighted value, and summing up each of the multiplied values, each degree based on the image feature value, the sound feature value, and the emotion feature value, respectively.