Research on image compression encoding based on fixed dictionary - Lifeng Li; Yanli Du; Yan-Hong Liu; Hua Yang (Systems Science & Control Engineering)
With the widespread application of IoT technology, a large amount of image data must be transmitted through networks. Owing to the current limited bandwidth, it is necessary to compress images to satisfy the requirements for real-time image transmission. Considering its innovative characteristics, a high compression ratio,and fractal compression techniques are usually adopted to code and decode images. The application of traditional fractal compression techniques is constrained by a long encoding time and low decoding accuracy, which are reduced by each image corresponding to one codebook. To address this issue, a fractal dictionary encoding (FDE) algorithm is proposed in this study. First, images with different shapes and textures were generated using a Julia fractal set(denoted as J set). Second, the generated images were segmented into a fixed-size set. A set of image blocks was obtained by expanding with a fixed-size set. Third, a fixed dictionary is created by classifying the image blocks using block truncation coding (BTC) values. Finally, the experimental results show that the FDE algorithm has a high compression ratio, high decoding accuracy and a very fast encoding and decoding speed, averaging 70 and 17 times faster than traditional fractal encoding(TFE) algorithms. The proposed algorithm satisfies the requirements for image compression.