Lossless JPEG

About: Lossless JPEG is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 51110 citations. The topic is also known as: Lossless JPEG & .jls.

Predicting dc using ac coefficients for JPEG coding

Abstract: The JPEG baseline algorithm codes the dc of a block by giving its difference with the dc of the previous block. We propose to use ac coefficients for this purpose. Our method computes the difference of the sum of pixels of two boundary columns (or rows), one belonging to the current block and the other to a previous block, and then manipulates it in the direct cosine transform (DCT) domain so that the average of the coded differences for the whole image is near zero. Experimental results show that our method reduces the average JPEG dc residual by about 75% for images compressed at the default quality level. The reduction is even higher for unquantized DCT blocks.

An Upper Limit of AC Huffman Code Length in JPEG Compression

Abstract: A strategy for computing upper code-length limits of AC Huffman codes for an 8x8 block in JPEG Baseline coding is developed. The method is based on a geometric interpretation of the DCT, and the calculated limits are as close as 14% to the maximum code-lengths. The proposed strategy can be adapted to other transform coding methods, e.g., MPEG 2 and 4 video compressions, to calculate close upper code length limits for the respective processing blocks.

Determining the Quality of Compression in High Resolution Satellite Images Using Different Compression Methods

Abstract: Measuring the quality of image is very complex and hard process since the opinion of the humans are affected by physical and psychological parameters. So many techniques are invented and proposed for image quality analysis but none of the methods suits best for it. Assessment of image quality plays an important role in image processing. In this paper we present the experimental results by comparing the quality of different satellite images (ALOS, RapidEye, SPOT4, SPOT5, SPOT6, SPOTMap) after compression using four different compression methods namely Joint Photographic Expert Group (JPEG), Embedded Zero tree Wavelet (EZW), Set Partitioning in Hierarchical Tree (SPIHT), Joint Photographic Expert Group – 2000 (JPEG 2000). The Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) values are calculated to determine the quality of the high resolution satellite images after compression.

A hybrid lossless compression technique with segmentation and modified arithmetic coding

Abstract: Compression of medical images (MI) is an important field of study in biomedical engineering. Although lossy compression could solve storage space and transmission bandwidth problems, it is not recommended by most physicians because of data loss. Lossless compression saves all details inside image; however it could not be applied for the whole image area. This paper introduces a hybrid lossless compression channel with two different steps. The first is an automatic segmentation technique, where a region of interest (ROI) is automatically segmented by aid of an artificial neural network (ANN) and an introduced difference fuzzy model (IDFM). The second is a modified arithmetic coding (MAC) lossless compression algorithm. This hybrid channel is to combine in parallel with a lossy compression channel that transmits non important parts of the MI progressively, using the fast algorithm of embedded zerotree wavelet (FEZW) [1]. The proposed technique reduces complexity, storage space, bandwidth, and saves time. Moreover, it is a fully automatic system. Several brain magnetic resonance images (MRI) and fluorescene ophthalmic images are analyzed.