To smooth the video content caused by handheld devices, this paper designs a hardware-oriented engine for efficient video stabilization. This engine is realized based on the motion energy computation. Maximum a posteriori estimation derives the global motion. Significantly, the motion energy smoothing accomplishes video stabilization. The global motion is smoothed by calculating the continuous and curve energy of successive frames. In addition, to achieve real-time video stabilization, efficient hardware architecture is proposed. The novel data reuse scheme is designed for enhancing the speed of corner point detection. The estimation skip technique is manipulated for lowering the computation of local motion estimation. Double buffering and pipeline running is designed for efficiently deriving the global motion. With these approaches, the corresponding hardware architecture has the characteristics of high efficiency and high throughput. The experimental results show that the proposed video stabilization engine can produce well-smooth videos and have high precision. The proposed hardware architecture enhances the performance of video stabilization with real-time and large resolution-processing ability. The objective comparison also demonstrates our good performance on video stabilization.

Today, more superimposed text is embedded within videos. Usually some text is unnecessary. Thus, one requires an approach to remove the text and complete the video. However, few conventional approaches complete the video well due to the large-sized text, structure regions, and various types of videos. In response, this study designed a text-video completion algorithm that poses text-video completion as structure repair and texture propagation. To repair the structure regions, the structure interpolation uses the new model's rotated block matching to estimate the initial location of completed regions and later refine the coordinates of completed regions. The information in the neighboring frames then fills the structure regions. To complete the structure regions without tedious manual interaction, the structure extension utilizes the spline curve estimation. Afterwards, derivative propagation realizes the texture region completion. The experiment results are based on several real TV programs, where all of the text regions were completed with spatio-temporal consistency. Additionally, comparisons present that the performance of the proposed algorithm is superior to those of conventional approaches. Its advantages include the reduction of design complexity by only integrating the structure information in multi-frame and the demonstration of structure consistency for realistic videos.

A request to simultaneously comprehend the video content of multi-channels has become. Therefore, a content-integration system was designed by extracting and integrating the embedded videotexts with other video content. This system was developed on a dual-core platform. For speeding up videotext extraction on hardware platforms, a data-aware transfer framework was proposed. In this framework, adaptive-size blocks are transferred based on the computing data, and single instruction multiple data (SIMD) architectures were manipulated to perform parallel data fetch and computation. The evaluation results demonstrated that the extracted videotexts from the informative channel can be integrated with the main-channel content, and the framework can significantly reduce the number of required cycles in the dual-core architecture. The comparison also presented that the performance of the proposed framework is higher than that of other approaches. Therefore, the proposed frameworks help to advance the data transfer in dual-core platforms and to benefit content integration.

Many videotexts exist in TV programs. Some videotexts provide valuable information. Thus, an efficient design to extract these videotexts is requested. Existing videotext extractors work on the PC platform and they are difficult to achieve real-time extraction and integration. Therefore, this work designs a videotext extractor on a dual-core platform. A distributed design framework for a dual-core platform is proposed. The extraction task is dispatched to the ARM and the DSP. The ARM core executes capture, display, control, and extraction threads. The DSP core performs algorithms. The ARM and the DSP communicate by buffers and solid channels. On the DSP side, some techniques are manipulated to optimize the videotext extractor. They include software pipeline, internal memory, adjusted program, assembly optimization, and DMA. To achieve high performance, two transferred schemes of DMA are proposed. This system is implemented on the TI Davinci DM6446 platform. All input videos are 720 times 480 with 30 fps captured from real-time DVB-T system. The simulation result shows that this extractor can process the large-size frames, and all the videotext can be extracted. With this novel architecture, the extraction speed can be enhanced to 23 frames per second.

With the rapid spread of video content, some embedded videotexts are superfluous advertisements needing an approach to remove. Because conventional video completion methods only deal with the limited video repairing, an adaptive advertisement video completion algorithm recovering various types of large and structural regions is designed. It poses the task of videotext removal as a hierarchical model. At the top of the hierarchy, the rotated block matching is derived, and temporal structure is recovered by the adaptive interpolation algorithm. Simultaneously, spatial structure is completed by the extension algorithm. At the bottom of the hierarchy, the spatial texture region completion is carried out by gradient derivative smooth propagation and duplication. The contribution is its comprehension and applicability to various types of videos. The experimental results show that all of the various videotext regions can be completed with temporal and spatial consistency, and the performance is superior to other existing methods.