Abstract: A 2D video to 3D video conversion system includes a video content analysis unit, a depth estimation unit, a post-processing unit, and a stereoscopic video generation unit. The video content analysis unit can analyze a 2D video datum and extract useful information including motion and color from the 2D video datum for depth estimation. The depth estimation unit is adapted for receiving the useful information, calculating motion cue and contrast cue for initial depth estimation, and generating an initial depth map. The post-processing unit is adapted for correcting the initial depth map in spatial domain and temporal domain to increase accuracy in spatial domain and depth continuity between adjacent time instances and for processing the caption in the video to generate a final depth map. The stereoscopic video generation unit is adapted for synthesizing 3D video datum from the final depth map and 2D video datum.

Abstract: A 2D video to 3D video conversion system includes a video content analysis unit, a depth estimation unit, a post-processing unit, and a stereoscopic video generation unit. The video content analysis unit can analyze a 2D video datum and extract useful information including motion and color from the 2D video datum for depth estimation. The depth estimation unit is adapted for receiving the useful information, calculating motion cue and contrast cue for initial depth estimation, and generating an initial depth map. The post-processing unit is adapted for correcting the initial depth map in spatial domain and temporal domain to increase accuracy in spatial domain and depth continuity between adjacent time instances and for processing the caption in the video to generate a final depth map. The stereoscopic video generation unit is adapted for synthesizing 3D video datum from the final depth map and 2D video datum.

Abstract: A personalized ranking method of audio and/or video data on Internet includes the steps of locating a plurality of audio and/or video data corresponding to at least one keyword; deciding a user index by the user or picking a history behavior index if the user does not decide the user index; capturing characteristics from the aforesaid downloaded audio and/or video data according to the user index or the history behavior; comparing the captured characteristics with a user profile or a history behavior for similarity to get a similarity score; ranking the audio and/or video data according to the corresponding similarity scores to get a ranking outcome of the audio and/or video data.

Abstract: A motion picture depth information processing system for processing depth information of a plurality of frames in a motion picture segment, each of which has corresponding color information, is composed of a buffer, a motion estimation unit, a motion compensation unit, and an adaptive post-filtering unit. The system can carry out motion estimation by referring to the color information of the frames to obtain a motion vector for each block in a frame and depth motion compensation based on the aforementioned motion vectors to get an initial depth map for each frame in the segment, then do a post-processing via the adaptive post-filtering unit to get a refined depth map of every frame, and finally generate more definite depth information for the whole motion picture segment, thus reducing geometrical texturing burrs, which are commonly known in the depth processing prior art technology.

Abstract: A depth calculating method is provided for calculating corresponding depth data in response to frame data, which includes macroblocks. The depth calculating method includes the following steps. First, a type of video is decided according to a video content. A motion vector is obtained from decompressed video information and is modified according to a shot change detection and camera motion data. Then, multiple pieces of macroblock motion parallax data respectively corresponding to the macroblocks are found according to motion vector data of the modified macroblocks. Thereafter, the depth data corresponding to the frame data is calculated according to the pieces of macroblock motion parallax data, variance data, contrast data and texture gradient data.

Abstract: A method for generating candidate encoding modes for an extended-channel video data subset of a stereo video data set includes the steps of: generating, for each macroblock of each frame of the extended-channel video data subset, a forward time difference image feature parameter set with reference to pixel values of pixels of the macroblock and a corresponding macroblock of a corresponding preceding frame; generating, for each macroblock, a plurality of first output values that respectively correspond to a plurality of predetermined possible block partition sizes with reference to the forward time difference image feature parameter set; and selecting, for each macroblock, a first number of candidate block partition sizes from the possible block partition sizes based on the first output values The candidate encoding modes include combinations of the first number of candidate block partition sizes and at least a part of a plurality of predetermined possible block estimation directions.