This paper proposes a novel surveillance video compression framework that does not modify the encoder and decoder. By introducing background blurring before encoding, the method significantly enhances compression efficiency and semantic regions’ signal. Background blurring is adopted to remove background texture and preserve signal of semantic regions(foreground) as a preprocessing step. This preprocessing yields higher compression gains and improves foreground signal simultaneously. This paper also presents the first quantitative model relating compression gain to ROI area ratio and blurring degree. It provides a theoretical basis for our approach's compression capability. Additionally, a video caching scheme is proposed to temporarily store original videos at the camera end. This enables lossless video retrieval in emergencies as a supply. Extensive experimental results are given and demonstrate our method's effectiveness and efficiency. At equivalent bitrates, the average PSNR of semantic regions increases about 1.22 dB. Our approach presents a simple but highly efficient solution for surveillance video compression without changing the encoder and decoder.