Different features that can be used to measure visual discontinuity Pixel differences

Two common approaches:

• Calculate pixel-to-pixel difference & Compare the sum with a threshold
• Count the number of pixels that change in value more than some threshold & Compare the total number against a second threshold.

Statistical differences

• Divide image into regions
• Compute statistical measures from these regions (e.g., mean, standard deviation)
• Compare the obtained statistical measures

Video Shot Boundary_decrypted-1

Compression differences

• Use differences in the discrete cosine transform (DCT) coefficients of JPEG compressed frames as the measure of frame similarity.
• Avoid the need to decompress the frames

Edges differences

• The edges of the objects in the last frame before the hard cut usually cannot be found in the first frame after the hard cut,
• The edges of the objects in the first frame after the hard cut in turn cannot be found in the last frame before the hard cut.
• Use Edge Change Ratio (ECR) to detect hard cuts Edges/contours: Based on edge change ratio (ECR). Let on be the number of edge pixels in frame n, and Xnin and Xn-1out the number of entering and exiting edge pixels in frames in frames n and n-1, respectively. The edge change ratio ECRn between frames n-1 and n is defined as:

Motion vector

Use motion vector to determine discontinuity.

Histograms comparison
The most common method used to detect shot boundaries.

Video Shot Boundary_decrypted-2

• Provides good trade-off between accuracy and speed
• The simplest histogram method computes gray level or color histograms of the two images. If the bin-wise difference between the two histograms is above a threshold, a shot boundary is assumed.
• Several extensions available: Using regions, region weighting, different distance metrics.

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