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An Optimized Quantization Technique for Image Compression Using Discrete Tchebichef Transform

机译:使用离散Tchebichef变换的图像压缩优化量化技术

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Discrete Tchebichef transform (DTT) has been utilized to improve the reconstruction quality of the traditional methods in image compression. Although DTT has the effective capability of energy concentration and ease of computation, not been exploited polynomials in orthogonal transform as compared with discrete cosine transform (DCT). This paper proposes an efficient lossy compression based DTT to produce better quality reconstructed image for the desired compression ratio. We combine soft decision quantization (SDQ) to design optimal quantization table and to approximate the rate-distortion for the purpose of the reconstruction quality. Compared with DCT under the scheme of JPEG baseline system, experimental results show that the proposed algorithm is of greater reconstruction image quality when the bit ratio exceeds 0.5 bpp. The bit ratio is decreased by 0.25, 0.49, 0.20 bpp, respectively when peak signal-to-noise-ratio (PSNR) is 35, 40, 45 dB. Meanwhile, they are similar on the elapsed time in encoding and decoding.
机译:离散Tchebichef变换(DTT)已被利用来改善图像压缩中传统方法的重建质量。尽管DTT具有能量浓度和易于计算的有效能力,但与离散余弦变换(DCT)相比,在正交变换中未被利用多项式。本文提出了一种基于高效的有损压缩的DTT,以产生所需压缩比的更好质量的重建图像。我们将软判决量化(SDQ)与设计最佳量化表相结合,并近似于重建质量目的的速率失真。与JPEG基线系统方案下的DCT相比,实验结果表明,当比特比超过0.5bpp时,该算法具有更大的重建图像质量。当峰值信噪比(PSNR)为35,40,45dB时,比特比分别减小0.25,0.49,0.20bpp。同时,它们在编码和解码中的经过时间相似。

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