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.

Near lossless image compression using lossless Hartley like transform

Abstract: It is shown that the discrete Hartley transform (DHT) of length N = 4 can be used to perform an integer-to-integer transformation. This behavior of the DHT can be used to compute a 2-D separable lossless Hartley like transform (L-HLT). The performance of the L-HLT is found to be better than the DCT for near lossless image compression and wavelet-based filter bank approach for lossless compression.

A Video-rate Jpeg Chip Set

Abstract: A two-chip set has been designed, fabricated and is fully functional which performs the baseline JPEG image compression and decompression algorithm. The major functions of the devices include: DCT and IDCT, forward and inverse quantization, Huffman coding and decoding. The devices operate with pixel rates beyond 30 MHz at 70 degrees C and 4.75 V. Each die is less than 10 mm on a side and was implemented in a 1.0 µ CMOS cell-based technology to achieve a 9 man-month design time.

Saliency-driven image coding improves overall perceived JPEG quality

Abstract: Saliency-driven image coding is well worth pursuing. Previous studies on JPEG and JPEG2000 have suggested that region-of-interest coding brings little overall benefit compared to the standard implementation. We show that our saliency-driven variable quantization JPEG coding method significantly improves perceived image quality. To validate our findings, we performed large crowdsourcing experiments involving several hundred contributors, on 44 representative images. To quantify the level of improvement, we devised an approach to equate Likert-type opinions to bitrate differences. Our saliency-driven coding showed 11% bpp average benefit over the standard JPEG.

Flexible Access Control to JPEG 2000 Image Code-Streams

Abstract: JPEG 2000 is an international standard for still image compression in the 21st century. Part 8 of the standard, named JPSEC, is concerned with all the security aspects, in particular to access control and authentication. This paper presents a novel access control scheme for JPEG 2000 image code-streams. The proposed scheme is secure against collusion attacks and highly efficient. The scheme is also very flexible, allowing access control to JPEG 2000 image code-streams according to any combination of resolution, quality layer and region of interest. The "encrypt once, decrypt many ways" property of our scheme is designed to work seamlessly with the "compress once, decompress many ways" feature of the JPEG 2000 image code-streams. Our prototype implementation shows that the scheme is practical and is completely compatible with the core part of the JPEG 2000 standard.

A Novel Adaptive Compression Technique for Dealing with Corrupt Bands and High Levels of Band Correlations in Hyperspectral Images Based on Binary Hybrid GA-PSO for Big Data Compression

Abstract: sensors generate useful information about cli- mate and the earth's surface in numerous contiguous narrow spectral bands, being widely used in resource management, agriculture, environmental monitoring, among others. The compression of hyperspectral data helps in long-term storage and transmission systems. This paper introduces a new adap- tive compression method for hyperspectral data. The method is based on separating the bands with different specifications by the histogram analysis and Binary Hybrid Genetic Algo- rithm-Particle Swarm Optimization (BHGAPSO). The new proposed method improves the compression ratio of the best- known JPEG standards, saves storage space, and speeds up the transmission system. The proposed method is applied on two different test cases, and the results are evaluated and compared with a few powerful compression techniques, such as lossless JPEG and JPEG2000. The results confirm that the proposed method is accurate, simple and fast, which can be useful for big data (i.e, a high volume of data) processing.