基于主观质量的JPEG XR量化参数选择  

Quantization Parameters Selection of JPEG XR Based on Subjective Quality

作　　者：刘致远[1] 陈耀武[1] 

机构地区：[1]浙江大学嵌入式系统研究开发中心,杭州310027

出　　处：《计算机工程》2014年第1期239-245,共7页Computer Engineering

基　　金：国家自然科学基金资助项目(40927001);浙江省重点科技创新团队计划基金资助项目(2011R09021-02)

摘　　要：在JPEG XR图像标准的基础上,提出一种提高其压缩效率的编码方法。该方法利用人类视觉系统对图片的感知特点,设计基于图像内容的自适应量化参数选择算法。根据最小可觉差模型,以图像的局部纹理和局部亮度为参数,将图像压缩过程中的宏块分为6类,对每类宏块的直流、低频、高频系数赋予不同的量化参数,从而使得整幅图像的码率根据纹理复杂度和亮度合理分布,在保持主观质量不变的情况下,减小图像码率,最终提高压缩效率。实验结果表明,相对于固定量化参数算法,该算法可使图像压缩效率得到最高10%的提升。This paper proposes an encoding method with improving compression efficiency based on the standard of JPEG XR image. This method designs an adaptive quantization parameters selection algorithm based on image content by using the perception features of Human Visual System（HVS）. According to the Just Noticeable Difference（JND） model, the macro blocks in the process of image compression are divided into 6 types by local texture and local brightness. Each type is assigned different quantization parameters of Direct Current（DC）, Low Pass（LP） and High Pass（HP） coefficients adaptively, which distributes the bit rate of the entire image reasonably according to the texture complexity and brightness. Therefore, higher compression efficiency and lower rate are achieved with the same subjective quality. Experimental result show the proposed algorithm obtains a 10% higher compression efficiency compared with fixed quantization parameters algorithm.

关 键 词：JPEGXR标准 主观质量 图像压缩 码率控制 人类视觉系统 最小可觉差 

分 类 号：TP37[自动化与计算机技术—计算机系统结构]