#Avidemux change frame rate mp4#
cut-kd: each video was manually cut through Kdenlive (v17.12.3) by keeping 5 to 7 seconds and then the video was saved with the MP4 -the dominating format(H264/AAC) setting.cut + downscale: each video was cut through ffmpeg and downscaled 10 to the resolution of 320 × 240.slow down: each video was cut through ffmpeg and slowed down 9.speed up: each video was speeded up 8 through ffmpeg.cut without re-encoding: each video was cut through ffmpeg by copying the audio and video coding parameters 7 to minimize the traces left by the operation.cut with re-encoding: each video was cut through ffmpeg and re-encoded 6.Experimental results show the proposed algorithm can improve the accuracy of BME and provide better objective and subjective interpolation qualities when compared with the traditional MVS algorithms. By performing CA-based evolution in a loop iteration, we gradually expose MV outliers and reduce incorrect MVs resulting from oversmoothing as many as possible. In our work, a cellular automaton is constructed to deduce MV outliers according to a defined local evolution rule. To overcome this problem, we propose a Cellular Automata-based MVS (CA-MVS) algorithm to smooth the Motion Vector Field (MVF) output by BME. The existing Motion Vector Smoothing (MVS) works often oversmooth or revise correct MVs as wrong ones. The instability of temporal symmetry results in many incorrect Motion Vectors (MVs) for Bidirectional Motion Estimation (BME) in MCFI. Motion-Compensated Frame Interpolation (MCFI) is one of the common temporal-domain tamper operations, and it is used to produce faked video frames for improving the visual qualities of video sequences. Five open MC-FRUC softwares and six representative MC-FRUC techniques have been tested, and experimental results show that the proposed approach can effectively locate interpolated frames and further identify the adopted MC-FRUC technique for both uncompressed videos and compressed videos with high perceptual qualities. Then, spatial and temporal Markov statistics features (ST-MSF) are extracted from the residual signals inside interpolated frames for MC-FRUC identification.
A pre-classifier is designed to suppress the side effects of original frames and static interpolated frames in candidate videos. The identification of various MC-FRUC techniques is then converted into a problem of discriminating the differences of residual signals among them. A theoretical model is firstly built for residual signal, which is exploited as tampering trace for blind forensics. A blind forensics approach is proposed for the identification of various MC-FRUC techniques. However, MC-FRUC can also be exploited to fake high frame rate videos or splice two videos with different frame rates for malicious purposes. Motion-compensated frame rate up-conversion (MC -FRUC) is originally presented to increase the motion continuity of low frame rate videos by periodically inserting new frames, which improves the viewing experience.
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