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.

Lossless Compression of Data Tables in Mobile Devices by Using Co-clustering

Abstract: Data tables have been widely used for storage of a collection of related records in a structured format in many mobile applications. The lossless compression of data tables not only brings benefits for storage, but also reduces network transmission latencies and energy costs in batteries. In this paper, we propose a novel lossless compression approach by combining co-clustering and information coding theory. It reorders table columns and rows simultaneously for shaping homogeneous blocks and further optimizes alignment within a block to expose redundancy, such that standard lossless encoders can significantly improve compression ratios. We tested the approach on a synthetic dataset and ten UCI real-life datasets by using a standard compressor 7Z. The extensive experimental results suggest that compared with the direct table compression without co-clustering and within-block alignment, our approach can boost compression rates at least 21% and up to 68%. The results also show that the compression time cost of the co-clustering approach is linearly proportional to a data table size. In addition, since the inverse transform of co-clustering is just exchange of rows and columns according to recorded indexes, the decompression procedure runs very fast and the decompression time cost is similar to the counterpart without using co-clustering. Thereby, our approach is suitable for lossless compression of data tables in mobile devices with constrained resources.

Image Compression Technique under JPEG by Wavelets

Abstract: It is used specially for the compression of images where tolerable degradation is required. With the wide use of computers and consequently need for large scale storage and transmission of data, efficient ways of storing of data have become necessary. With the growth of technology and entrance into the Digital Age, the world has found itself amid a vast amount of information. Dealing with such enormous information can often present difficulties. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.JPEG and JPEG 2000 are two important techniques used for image compression.JPEG image compression standard use dct (discrete cosine transform). Now there wavelets Transform is using with JPEG 2000 standard. It is a widely used and robust method for image compression. It has excellent compaction for highly correlated data. Wavelets transform divided the image into high frequency components. Which gives good compromise between information packing ability and computational complexity?

A low power high performance design for JPEG Huffman decoder

Abstract: JPEG image codec is one of the most commonly used standards for communication and storage applications. In this paper, we propose a simple and successful design for a JPEG alternating current Huffman decoder with low power and high performance considerations. Based on the parallel Huffman decoder structure, we utilize the bipartition architecture on the lookup table to reduce power consumption without sacrificing decoding performance. Gate level power simulation results show a maximum of 25% power reduction can be obtained when compared with the conventional JPEG parallel Huffman decoder.

Codestream-Based Identification of JPEG 2000 Images with Different Coding Parameters

Abstract: A method of identifying JPEG 2000 images with different coding parameters, such as code-block sizes, quantization-step sizes, and resolution levels, is presented. It does not produce false-negative matches regardless of different coding parameters (compression rate, code-block size, and discrete wavelet transform (DWT) resolutions levels) or quantization step sizes. This feature is not provided by conventional methods. Moreover, the proposed approach is fast because it uses the number of zero-bit-planes that can be extracted from the JPEG 2000 codestream by only parsing the header information without embedded block coding with optimized truncation (EBCOT) decoding. The experimental results revealed the effectiveness of image identification based on the new method.