Unsupervized Video Summarization using RL.

Reward= Reward_Diversity + Reward_Representativeness.

Reward_Representativeness = Pixel Difference between two consecutive images ( MSE between them)

Changing it to

Reward_Representativeness = SSIM between two consecutive images

Where

SSIM : structural similarity (SSIM) index

luminance (light emitted) , contrast( difference b/w luminance) , image degradation()

Results (Initial) :

Number	Video	NewMetric	Orignal_Metric
1	video_14	28.4%	28.4%
2	video_19	60.8%	28.6%
3	video_23	58.3%	61.7%
4	video_25	55.3%	55.3%
5	video_7	29.2%	29.1%

Number	Video	NewMetric	Orignal_Metric
1	video_13	29.2%	50.2%
2	video_16	34.9%	34.9%
3	video_24	29.5%	29.5%
4	video_3	35.2%	35.2%
5	video_4	54.4%	47.7%

The Algorithm uses preprocessed data as its input .

Right now , working on getting from raw data to output + into matrix that what causes these changes.