隨著科技的進步，更快更可靠的網路使得物聯網成為可能。而伴隨著物聯網科技的發展而來的，是如何處理從終端裝置中，所收集的巨量資料。
本文希望能藉由影像處理，利用分解與比對來自物聯網裝置的串流影片，找出在一定影片檔案壓縮比率之下，最佳的影片品質。除此之外，我們在實驗中還能夠藉由操作不同的比較參數，讓影片壓縮的演算法能夠在不同的情境下，藉由調整參數權重，找出最適合該情境的壓縮演算法配置，藉此達到減少儲存空間需求的目的。在實驗中，我們建立基本的物聯網串流應用，並且能夠在與原影片96.5%的差異度之下，達到40%的影格儲存空間精簡優化。

The progression of cheaper, faster and more reliable Internet technology makes Internet of Things (IoT) realized in life. While tremendous data are collected from end devices, scalable and effective data compression techniques are needed to balance storage and precision. This paper presents an adjustable content-sensitive data compression approach for IOT streaming services and applications. Specifically, we apply frame similarity on various aspects such as illumination and structure to streaming frames, and are able to keep sufficient differences among steaming data while reducing significant amount of storage. We setup a general iot application platform in practice and show that with the presented approach, we are able to keep 96.5% precision with 40% frame reduction on the steaming data collected in real life.

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