针对基于结构相似性的帧间组稀疏表示重构 (SSIM-InterF-GSＲ) 算法在重构平稳区域时未能充分利用高质量重构的关键帧信息，且稀疏化处理阈值的数值设置不合理的问题，提出了基于块分类的自适应阈值调整组稀疏重构 (BC-ATA-GSR) 算法。首
先，根据块内物体运动状态分类图像块并分配合理的参考帧，以提高视频序列平稳区域的重构质量; 然后，根据采样率以及图像块种类自适应设置稀疏化初始阈值，以保留足够的结构信息; 最后，提出了迭代阈值梯度缩减方案，以便在提升迭代后期重构质量的同时也加快迭代收敛速度。与 SSIM-InterF-GSＲ 算法相比，BC-ATA-GSR 算法取得了更好的重构质量，重构 QCIF 和 CIF 视频序列的平均 PSNR 分别最高提升了 3. 77、2. 28dB，时间复杂度最多下降了 42. 08%。

Aiming at the problems that structural similarity based inter-frame group sparse representation (SSIM-In-terF-GSR) algorithm can＇t fully utilize the high-quality reconstructed key frame information when reconstructing the smooth region and the sparse processing threshold setting is unreasonable，block classification-based adaptive threshold adjustment group sparse reconstruction (BC-ATA-GSR) algorithm was proposed in this paper． Firstly，image blocks were classified into smooth blocks and motion blocks according to the motion state of the objects in the blocks，and reasonable reference frames were allocated for different types of blocks to improve the reconstruction quality of the smooth regions in the video sequence． Then，in order to retain the sufficient structural information，the initial threshold of sparseness was set adaptively according to the sampling rate and the image block type． Fina-lly，an iterative threshold gradient reduction scheme was proposed to accelerate the iterative convergence rate and improve the quality of reconstruction． Compared with SSIM-InterF-GSＲ algorithm，BC-ATA-GSR algorithm achieves better reconstruction effect，and the average PSNR of the reconstructed QCIF and CIF video sequence are increased up to 3. 77dB and 2. 28dB respectively，and the time complexity is reduced up to 42. 08%．