Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA.
IEEE Trans Image Process. 2013 Apr;22(4):1326-39. doi: 10.1109/TIP.2012.2228492. Epub 2012 Nov 20.
This article presents four modifications to the JPEG arithmetic coding (JAC) algorithm, a topic not studied well before. It then compares the compression performance of the modified JPEG with JPEG XR, the latest block-based image coding standard. We first show that the bulk of inter/intra-block redundancy, caused due to the use of the block-based approach by JPEG, can be captured by applying efficient prediction coding. We propose the following modifications to JAC to take advantages of our prediction approach. 1) We code a totally different DC difference. 2) JAC tests a DCT coefficient by considering its bits in the increasing order of significance for coding the most significant bit position. It causes plenty of redundancy because JAC always begins with the zeroth bit. We modify this coding order and propose alternations to the JPEG coding procedures. 3) We predict the sign of significant DCT coefficients, a problem is not addressed from the perspective of the JPEG decoder before. 4) We reduce the number of binary tests that JAC codes to mark end-of-block. We provide experimental results for two sets of eight-bit gray images. The first set consists of nine classical test images mostly of size 512 × 512 pixels. The second set consists of 13 images of size 2000 × 3000 pixels or more. Our modifications to JAC obtain extra-ordinary amount of code reduction without adding any kind of losses. More specifically, when we quantize the images using the default quantizers, our modifications reduce the total JAC code size of the images of these two sets by about 8.9 and 10.6%, and the JPEG Huffman code size by about 16.3 and 23.4%, respectively, on the average. Gains are even higher for coarsely quantized images. Finally, we compare the modified JAC with two settings of JPEG XR, one with no block overlapping and the other with the default transform (we denote them by JXR0 and JXR1, respectively). Our results show that for the finest quality rate image coding, the modified JAC compresses the large set images by about 5.8% more than JXR1 and by 6.7% more than JXR0, on the average. We provide some rate-distortion plots on lossy coding, which show that the modified JAC distinctly outperforms JXR0, but JXR1 beats us by about a similar margin.
本文提出了 JPEG 算术编码（JAC）算法的四项修改，这是一个以前研究不多的主题。然后，将修改后的 JPEG 与最新的基于块的图像编码标准 JPEG XR 的压缩性能进行了比较。我们首先表明，由于 JPEG 使用基于块的方法，块内/块间冗余的大部分可以通过应用有效的预测编码来捕获。我们为 JAC 提出了以下修改，以利用我们的预测方法。1）我们编码一个完全不同的 DC 差值。2）JAC 通过按重要性从高到低的顺序测试 DCT 系数，为最显著位位置编码，从而导致大量冗余，因为 JAC 总是从第零位开始。我们修改了这种编码顺序，并对 JPEG 编码过程提出了交替。3）我们预测显著 DCT 系数的符号，这是 JPEG 解码器以前没有从角度解决的问题。4）我们减少了 JAC 编码以标记块结束的二进制测试次数。我们为两组八位灰度图像提供了实验结果。第一组由九张经典测试图像组成，大小大多为 512×512 像素。第二组由大小为 2000×3000 像素或更大的 13 张图像组成。我们对 JAC 的修改在不增加任何损失的情况下获得了额外的代码减少。更具体地说，当我们使用默认量化器对图像进行量化时，我们的修改分别将这两组图像的 JAC 总码大小减少了约 8.9%和 10.6%，JPEG 哈夫曼码大小减少了约 16.3%和 23.4%，平均而言。对于粗量化的图像，增益甚至更高。最后，我们将修改后的 JAC 与 JPEG XR 的两种设置进行了比较，一种没有块重叠，另一种具有默认变换（我们分别将它们表示为 JXR0 和 JXR1）。我们的结果表明，对于最佳质量率图像编码，修改后的 JAC 平均比 JXR1 压缩大组图像多约 5.8%，比 JXR0 多约 6.7%。我们提供了一些有损编码的率失真图，这些图表明修改后的 JAC 明显优于 JXR0，但 JXR1 以类似的优势击败了我们。
