It　has　always　been　a　major　issue　for　a　hospital　to　acquire　real-time　information　about　a　patient　in　emergency　situations.　Because　of　this,　this　research　presents　a　novel　high-compression-ratio　and　real-time-process　image　compression　very-large-scale　integration　(VLSI)　design　for　image　sensors　in　the　Internet　of　Things　(IoT).　The　design　consists　of　a　YEF　transform,　color　sampling,　block　truncation　coding　(BTC),　threshold　optimization,　sub-sampling,　prediction,　quantization,　and　Golomb-Rice　coding.　By　using　machine　learning,　different　BTC　parameters　are　trained　to　achieve　the　optimal　solution　given　the　parameters.　Two　optimal　reconstruction　values　and　bitmaps　for　each　4　x　4　block　are　achieved.　An　image　is　divided　into　4　x　4　blocks　by　BTC　for　numerical　conversion　and　removing　inter-pixel　redundancy.　The　sub-sampling,　prediction,　and　quantization　steps　are　performed　to　reduce　redundant　information.　Finally,　the　value　with　a　high　probability　will　be　coded　using　Golomb-Rice　coding.　The　proposed　algorithm　has　a　higher　compression　ratio　than　traditional　BTC-based　image　compression　algorithms.　Moreover,　this　research　also　proposes　a　real-time　image　compression　chip　design　based　on　low-complexity　and　pipelined　architecture　by　using　TSMC　0.18　mu　m　CMOS　technology.　The　operating　frequency　of　the　chip　can　achieve　100　MHz.　The　core　area　and　the　number　of　logic　gates　are　598,880　mu　m(2)　and　56.3　K,　respectively.　In　addition,　this　design　achieves　50　frames　per　second,　which　is　suitable　for　real-time　CMOS　image　sensor　compression.