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 image compression based on a generalized recursive interpolative DPCM

Abstract: It is shown that the generalized recursive interpolation algorithm (GRINT) proposed is the most effective hierarchical technique for reversible compression of images that typically occur in remote sensing. The main advantage of the novel scheme with respect to other noncausal DPCM schemes is that interpolation is performed from all error-free values, thereby reducing the variance of residuals. Tests on LandSat, NOAA/AVHRR, MeteoSat, and SPOT images show that GRINT outperforms established hierarchical techniques, and also lossless JPEG and optimum DPCM when dealing with SPOT data, with the further advantage that GRINT makes error-free tokens available at any resolution, thereby expediting remote browsing on large image data-bases.

Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain

Abstract: JPEG is a popular image compression method widely used by individuals, data center, cloud storage and network filesystems. However, most recent progress on image compression mainly focuses on uncompressed images while ignoring trillions of already-existing JPEG images. To compress these JPEG images adequately and restore them back to JPEG format losslessly when needed, we propose a deep learning based JPEG recompression method that operates on DCT domain and propose a Multi-Level Cross-Channel Entropy Model to compress the most informative Y component. Experiments show that our method achieves state-of-the-art performance compared with traditional JPEG recompression methods including Lepton, JPEG XL and CMIX. To the best of our knowledge, this is the first learned compression method that losslessly transcodes JPEG images to more storage-saving bitstreams.

JPEG Steganalysis Using the Relations Between DCT Coefficients

Abstract: Increasing attention to steganalysis and steganography due to the need for secure information transfer is one of the most important concerns of communication. Among the several image formats, JPEG is the most widely used compression method today. As a result, various stenographic systems based on disguising messages in jpeg format have been presented. Consequently, steganalysis of JPEG images is very essential. Recently, using neural networks and deep learning has greatly increased both in spatial and JPEG steganalysis. However, in the field of JPEG steganalysis, most of the existing networks still utilized hand-designed components as well. In the proposed JPEG steganalysis method we investigate the relations of the quantized Discrete Cosine Transform (DCT) coefficients and extract the binary vectors as the input of the neural network employing the relations of mid-frequency coefficients. The experimental results illustrate the acceptable detection rate of the simple presented approach.

Adaptive Algorithm Based on Reversible Data Hiding Method for JPEG Images

Abstract: This paper presents an adaptive reversible information hiding algorithm that can maintain thee JPEG file sizes by using RLC (Run Length Coding) AC coefficient coded for embedding, the key point is to choose the appropriate number of participation to hide information. By calculating the maximum storage capacity of the image at different system, select the appropriate RLC pairs to rotate and embed data. In the extraction stage, by calculating the sequence of the original RLC pairs status, then consult the mapping relationships between the current sequence and the original RLC pairs sequence, we extract the secret message and recover the original image. Test results proved that the proposed method can improve the rate-distortion performance to some extent.