Ternary content addressable memory (TCAM) is favorable for high-speed search due to its parallel architecture and ability for searching arbitrary-length keys. However, the usage of TCAM is limited because of its high cost and power consumption. This paper introduces a TCAM-based Huffman decoding algorithm for single-side growing Huffman tree (SGH-tree), which has been proposed to reduce the sparsity of traditional Huffman tree. Our scheme is based on the property, which leaves in the SGH-tree are highly concentrated. By extracting and searching the common prefixes of the codewords, the power consumption and the required storage of TCAM can be significantly reduced as well as its cost. In our experiments based on twelve real images, the power consumption is reduced to twentieth as compared to the original implementation.