This letter presents a low-complexity attention module for fast change detection. The proposed module computes the absolute difference between bi-temporal features extracted by a Siamese backbone network and sequentially applies spatial and channel attention to generate key change representations. Spatial attention emphasizes important spatial locations using representative values from channel-wise pooling, while channel attention highlights discriminative feature responses using values from spatial-wise pooling. By leveraging low-dimensional representative features, the module significantly reduces computational cost. Additionally, its dual-attention structure—driven by feature differences—enhances both spatial localization and semantic relevance of changes. Compared to the Change-Guided Network (CGNet), the proposed method reduces multiply-accumulate operations (MACs) by 53.81% with only a 0.15% drop in F1-score, demonstrating high efficiency with minimal performance degradation. These results suggest that the proposed method is suitable for large-scale or real-time remote sensing applications where computational efficiency is essential.

This paper proposes a three-dimensional transform (3DT) and its supporting tools: 3DT_Env. 3DT_Env improves H.264/AVC inter coding performance from four aspects: 1) H. 264/AVC coding is enhanced with the 3DT, 2) As the 3DT is proposed, a quantizer should be redesigned, 3) A new scanning method which considers the directions of INTER16x8 and INTER8x16 modes improves the efficiency of H.264/AVC, and 4) a threshold for cost calculation is optimized for the 3DT. Compared to H.264/AVC, the improvements can bring on average 4.07% coding gain for CABAC in high profile, with average coding gain of 5.34% for HD sequences.

The careless illegally copied contents have been rising serious social problem as Internet and multimedia technologies are developing. Therefore, effective and efficient copy detection techniques are required for content management and rights protection. In this paper, we propose a content-based hierarchical video copy detection method that estimates similarity using statistical characteristics between an original video and its spatial variations. We rank luminance average value of video that is robust to the special transformation, and choose similar video sequences named as candidate segments in huge amount of database to reduce processing time and complexity. Finally, we detect the copied video by using the hypothesis test of mean. As experiment result, proposed method has similar copy detection ratio accuracy to the reference method while our processing time and complexity are less than those of the reference since we can reduce the number of keyframes up to 50%. Also, the proposed method can efficiently detect spatial variations such as blur, contrast change, zoom in, and zoom out.

Matching pursuit has been shown to be useful especially in low bit-rate video coding. However, one practical concern in its application is the massive computation required for finding dictionary elements. This paper provides a new method that can drastically reduce the computational load without any degradation of image quality. We use a simple distance comparison based on the Schwarz inequality to preclude a substantial number of dictionary elements without actually evaluating inner products. Experimental results show the number of inner product calculations about 20-30% of the separability-based method by Neff and Zakhor (see IEEE Trans. Circuits and Systems for Video Technology, vol.7, p.158-71, Feb. 1997).

We proposed the post-processing method to reduce the blocking artifact in low-bit-rate coded JPEG images, which is based on minimum block-boundary-discontinuity criterion. We analyzed the statistical property of block boundary pixel difference, classified all blocks into one of two types using OSLD (overlapped sub-laplacian distribution), and compensated the quantization error depending on the type of block, adaptively. Our postprocessing method can provide better visual quality as well as PSNR than any other method.

Computational Geometry is concerned with the design and analysis of computational algorithms which solve geometry problems. In this paper SIMD-MCC algorithms for solving geometric searching problems such as the point domination, the point maxima, the range searching, and the closest point problems are designed by using the parallel divide-and-conquer technique. The computational complexities of all these algorithms for N input points in the k-dimensional space are O(CkN0.5), where Ck = (20.5 + 1)k-2.