Research on image compression encoding based on fixed dictionary

Research on image compression encoding based on fixed dictionary

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With the widespread application of IoT technology, a large amount of image data must be animed aniflex complete 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 equi-jec 6 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.