A Cross-Precision Motion Compensation Technique for Security Surveillance Video Coding

JIANG Wei; MA Wei; LU Jinghui; ZHANG Yue; ZHANG Yundong

doi:10.11999/JEIT251301

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JIANG Wei, MA Wei, LU Jinghui, ZHANG Yue, ZHANG Yundong. A Cross-Precision Motion Compensation Technique for Security Surveillance Video Coding[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251301

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JIANG Wei, MA Wei, LU Jinghui, ZHANG Yue, ZHANG Yundong. A Cross-Precision Motion Compensation Technique for Security Surveillance Video Coding[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251301

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A Cross-Precision Motion Compensation Technique for Security Surveillance Video Coding

doi: 10.11999/JEIT251301 cstr: 32379.14.JEIT251301

JIANG Wei^{1, 2, 3
,},
MA Wei^{2, 3},
LU Jinghui^{2, 3},
ZHANG Yue^{1, 3},
ZHANG Yundong^{1, 2, 3
,
,}

1.
School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
2.
Beijing Vimicro Artificial Intelligence Chip Technology Co., Ltd, Beijing 100191, China
3.
National Key Laboratory of Digital Perception Chip Technology, Beijing 100191, China

Received Date: 2025-12-08
Accepted Date: 2026-03-27
Rev Recd Date: 2026-03-27

Available Online: 2026-04-21

Abstract

Abstract

Objective High-altitude dome cameras are widely used in modern security surveillance. They are often deployed at critical locations, such as bridges and tower tops, where they are vulnerable to external interference. Such interference can cause jitter, blur, and distortion in captured videos, creating major challenges for video coding. In video compression, high-precision motion compensation is essential for improving coding efficiency. However, the existing Ultimate Motion Vector Expression (UMVE) technique has limited motion-vector precision and insufficient flexibility in adaptive adjustment. High-precision motion compensation tools, such as Registration Coding Mode (RCM) and Affine Motion Compensation Prediction (AFFINE), can improve compensation accuracy, but they require high computational complexity and hardware cost. These limitations make it difficult to meet the requirements for coding efficiency, power consumption, and real-time processing in high-altitude surveillance scenarios. Therefore, this study aims to design an optimized UMVE scheme that integrates high-precision motion compensation, low computational complexity, and scene adaptability to improve coding efficiency while balancing resource consumption. Methods This study proposes UMVE_CPMC, an Ultimate Motion Vector Expression technique supporting Cross-Precision Motion Compensation. The proposed method improves motion compensation accuracy by constructing an extended Up-Precision Motion Vector (UPMV), expressed as UPMV = BaseMV + MMV(p, angle). Here, Base Motion Vector (BaseMV) denotes the base vector obtained by the existing UMVE method, and Micro-Motion Vector (MMV) denotes the fine-adjustment vector defined by a specific precision p and angle. Incremental candidates are provided only at the 1/8 precision level to balance computational complexity and compression efficiency. For step-size adaptive adjustment, a six-mode improved scheme is proposed. It covers enhanced UMVE, conventional UMVE, and four precision-improved modes, allowing the encoder to switch flexibly according to scene characteristics. The average image gradient is used as an objective evaluation index. Test scenes are divided into Class A, representing high-clarity motion scenes, and Class B, representing low-clarity scenes. Different coding configurations, sequences, and parameters are used to compare coding gains and computational efficiency under different modes. Results and Discussions Experiments show that UMVE_CPMC improves performance under different scenes and modes. In Class A high-clarity motion scenes, with both the adaptive strategy and RCM disabled, the average gains of the Y, U, and V components in Fusion Mode 1 are –2.912%, –1.656%, and –1.654%, respectively. The average coding time is reduced to 94.55% of the baseline. In Independent Mode 1, the average Y-component gain reaches –2.925%, and the coding time is reduced to 91.91% of the baseline. Compared with conventional UMVE, when CPMC Independent Mode 1 is enabled with RCM and other tools working together, the gain improves from –0.276% to –1.310%, indicating higher cost effectiveness. In Class B low-clarity scenes, adaptive adjustment significantly reduces the losses of coding gain in Fusion Mode 1 and Fusion Mode 0. The average losses of coding gain are limited to 0.071% and 0.108%, respectively, which maintains the original coding gain. In multi-scene tests with RCM and AFFINE disabled, 9 of 10 test sequences in adaptive Fusion Mode 1 show positive gains. The Y-component gain reaches –10.691% for the yuxuedaolu sequence and –11.400% for the BQTerrace sequence. When all existing coding tools are enabled, the Y-component gains of the dianjing, yuxuedaolu, and BQTerrace sequences reach –1.29%, –2.05%, and –1.21%, respectively. The coding time is reduced to 94%～96% of the baseline. In addition, correlation analysis shows a clear positive relationship between the average image gradient and the coding gain. Images with a high average gradient, corresponding to high clarity, gain more from UMVE_CPMC, whereas images with a low average gradient, corresponding to low clarity, benefit little. Principle analysis shows that pixel changes in low-clarity images are smooth, so high-precision interpolation cannot generate effective new pixel values. The compensation effect is therefore limited. The performance differences among modes are consistent with their computational complexity. The fusion mode balances gain and stability, whereas the independent mode further reduces computation. The six step-size adaptive modes can meet the real-time and precision requirements of different scenes. Conclusions The proposed UMVE_CPMC technique integrates Cross-Precision Motion Compensation with the UMVE algorithm. It addresses the limited precision of conventional UMVE and the high computational complexity of high-precision motion compensation tools. It also achieves a favorable balance among coding efficiency, computational complexity, and scene adaptability. In Class A high-clarity motion scenes, UMVE_CPMC achieves notable coding gains. The gain exceeds 10% for some sequences when other high-precision motion compensation tools are disabled and reaches 1%～2% when used with other tools. In Class B low-clarity scenes, the original coding gain is maintained through a frame-level adaptive adjustment interface. In addition, the fusion mode does not increase hardware complexity, whereas the independent mode significantly reduces coding time. These features make the proposed method suitable for encoder designs with limited resources or simplified requirements. UMVE_CPMC provides an effective approach for improving the coding efficiency of high-altitude dome camera videos affected by jitter and blur. It also enriches the video coding toolset and provides practical guidance for optimizing video coding technologies in security surveillance. Future work will further optimize the adaptive strategy, explore integration with other advanced coding tools, develop scenario-specific coding schemes, and improve performance in complex scenes.
- Video coding and decoding,
- Cross-precision motion compensation,
- Motion vector expression,
- High-altitude dome camera

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References(18)

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