Image Compression Using Elimination and Dynamic Rules

Abstract

     This study proposes a method for developing the Triangular Inequality Elimination (TIE) rule and applying it in image compression based on block quantization. The study employed a dynamic rule to enhance and refine the TIE method. Applying the dynamic rule to the TIE algorithm reduces the computational complexity during the compression (coding-decoding) process, speeds up execution and transmission, and eliminates the need for large storage spaces. The dynamic rule is useful because it changes blocks close to the reference block during each attempt (S) to find the smallest number of blocks closest to the reference block. These blocks were then placed in a set. The higher the blocks in the set, the higher the image quality is at the expense of the compression ratio. The results of the dynamic rule that modifies the TIE algorithm are high compression ratios, quick implementation, low storage space for the image, and good reconstructed image quality. In other words, the suggested method, Triangular Inequality Elimination with Dynamic Rules (D-TIE), will significantly reduce the time required for matching searches; therefore, it is the least computationally demanding.

The dynamic rule is useful because it swaps blocks close to the reference block during each attempt (S) to find the smallest number of blocks closest to the reference block. These blocks are then placed in a final set. The fewer the number of blocks in the set, the higher the image quality at the expense of the compression ratio.

The results obtained by the dynamic rule that modifies the TIE algorithm are high compression ratios, quick implementation, low storage space, and good reconstructed image quality. In other words, the suggested method, Triangular Inequality Elimination with Dynamic Rules (D-TIE), will greatly reduce the time of matching searches; therefore, it is the smallest in computational difficulty.