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JPEG-LS:JLS1. M. J. Weinberger, G. Seroussi and G. Sapiro, “The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS,” IEEE Trans. on Image Processing, vol.9, pp. 1309 – 1324 , Aug. 2000. Website: http://www.hpl.hp.com/loco/HPL-98-193RI.pdf JLS2. J. Weinberger, G. Seroussi, and G. Sapiro, “LOCO-I: A low complexity, context-based, lossless image compression algorithm”, Hewlett-Packard Laboratories, Palo Alto, CA. JLS3. Ibid, “LOCO-I A low Complexity Context-based, lossless image compression algorithm”, Proc. 1996 DCC, pp.140-149, Snowbird, Utah, Mar. 1996. JLS4. See Z. Zhang, R Veerla and K.R. Rao, “A modified advanced image coding” CANS University Press, pp. 110-116, 2010. JLS5. UBC JPEG-LS codec implementation [online]. Available: http://www.stat.columbia.edu/~jakulin/jpeg-ls/mirror.htm JLS6.http://www.ece.ubc.ca/spmg/research/jpeg/jpeg_ls/jpegls.html JLS7. LOCO-I Software (public domain) (JPEG-LS) http://spmg.ece.ubc.ca/research/jpeg/jpeg_ls/jpegls.html JLS8. LOCO_I technical report www.hpl.hp.com/loco (HP Labs) JLS9. JPEG-LS ( LOCO-I) http://www.hpl.hp.com/loco/HPL-98-193R1.pdf PROJECT: JLS-P1. This paper involves comparison of various image coding standards such as JPEG, JPEG-LS, JPEG2000, JPEG XR, advanced image coding (AIC) and modified AIC (MAIC) (Also H.264/MPEG4 AVC intra mode only). Extend this comparison to HEVC intra mode only. Consider test images at various spatial resolutions and at different bit rates. Include BD-bit rate and BD-PSNR as metrics besides PSNR and SSIM. Consider also implementation complexity. JPEG:JPEG1. G. K. Wallace, “The JPEG still picture compression standard,” Commun. ACM, vol. 34, no. 4, pp. 30–44, Apr. 1991. Also “The JPEG still picture compression standard,” IEEE Trans. CE, vol. 38, pp. 18-34, Feb. 1992. JPEG2. Independent JPEG Group. [Online]. Available: http://www.ijg.org/ WWW.jpeg.org
JPEG9. T. Richter, “JPEG on steroids: Common optimization techniques for JPEG image compression”, IEEE ICIP2016, pp. , Phoenix, AZ, Sept. 2016. JPEG10. H. Lee et al, “A novel scheme for extracting a JPEG image from an HEVC compressed data set” IEEE ICCE, Las Vegas, Jan. 2017. JPEG11 C. Liu et al, “Random walk graph Laplacian-based smoothness prior for soft decoding of JPEG images”, IEEE Trans. IP, vol.26, pp.509-524, Feb 2017. BOSSbase image data set http://www.agents.cz/boss/BOSSFinal IMAGE DATA SET JPEG-P1 Oliveira et al [JPEG8] have developed a new low complexity approximation the discrete Tchebichef transform (DTT) and embedded it in JPEG and H.64/AVC. Extend this to 16-point DTT and develop a fast algorithm. Draw a flow graph similar to that shown in Fig.4. JPEG-P2 Develop hardware aspects of the 16-point DTT as described in VI Hardware section. This implies that the 16- point DTT needs to be implemented on Xilinx FPGA board. JPEG-P3 See JPEG-P1. Embed the new DTT in HEVC Main profile and compare its performance with anchor HEVC. (See Fig. 4) JPEG-P4 Huang et al [JPEG6] have proposed a new histogram shifting based RDH (reversible data hiding) scheme in JPEG images that realize high embedding capacity and good visual quality while preserving the JPEG file size. Implement this scheme and confirm the results as described in the figures. JPEG-P5 See JPEG-P4. In section IV Conclusions the authors state that the proposed novel block selection strategy can result in better visual quality and less JPEG file size. They also state that this technique may be applied to other RDH schemes to improve their performance. Several references related to RDH in images are listed at the end in [JPEG6]. Apply this strategy in the RDH schemes and evaluate their performances. Use different test images at various quality factors. (See also the references listed at the end in [JPEG7]). JPEG-P6 See JPEG10. Implement JPEG extraction from the HEVC bit stream in the compressed domain. Confirm the results shown in Table I for the test sequences. JPEG-P7 See [JPEG11].In conclusion he authors state “In future work, we will work on speeding up the proposed algorithm to make it more practical”. Explore this.JPEG XTJXT1. T. Richter et al, “The JPEG XT suite of standards: status and future plans,” [9599 – 30], SPIE. Optics + Photonics, San Diego, California, USA, 9 – 13, Aug. 2015. JXT2. M. Fairchild : “The HDR Photographic survey,” available online at http://rit-mcsl.org/fairchild/HDR.html (retrieved July 2015). JXT3. R. Mantiuk, “pfstools : High Dynamic Range Images and Video,” available online at http://pfstools.sourceforge.net/ (retrieved July 2015). JXT4. T. Ricther , “JPEG XT Reference Codec 1.31 (ISO License),” available online at http://www.jpeg.org/jpegxt/software.html (retrieved July 2015) JXT5. T. Richter, ”Lossless Coding Extensions for JPEG,” IEEE Data Compression Conference, pp. 143 -152, Mar. 2015. (See JXT1) JPEG XT is a standardization effort targeting the extension of the JPEG features by enabling support for high dynamic range imaging, lossless and near lossless coding and alpha channel coding, while also guaranteeing backward and forward compatibility with the JPEG legacy format. JPEG XT has nine parts described in detail in Fig. 2. Further extensions relate to JPEG Privacy and Security and others such as JP Search and JPEG systems and are listed in the conclusion. JPEG XT is forward and backward compatible with legacy JPEG unlike JPEG-LS and JPEG2000. JXT6 A. Artusi et al, “Overview and Evaluation of the JPEG XT HDR Image Compression Standard”, J. of real time image processing, vol. 10, Dec. 2015. Swaminathan s Abstract: Standards play an important role in providing a common set of specifications and allowing interoperability between devices and systems. Until recently, no standard for High Dynamic Range (HDR) image coding had been adopted by the market, and HDR imaging relies on proprietary and vendor specific formats which are unsuitable for storage or exchange of such images. To resolve this situation, the JPEG Committee is developing a new coding standard called JPEG XT that is backwards compatible to the popular JPEG compression, allowing it to be implemented using standard 8-bit JPEG coding hardware or software. In this paper, we present design principles and technical details of JPEG XT. It is based on a two-layers design, a base layer containing a Low Dynamic Range (LDR) image accessible to legacy implementations, and an extension layer providing the full dynamic range. The paper introduces three of currently defined profiles in JPEG XT, each constraining the common decoder architecture to a subset of allowable configurations. We assess the coding efficiency of each profile extensively through subjective assessments, using 24 naive subjects to evaluate 20 images, and objective evaluations, using 106 images with five different tone-mapping operators and at 100 different bit rates. The objective results (based on benchmarking with subjective scores) demonstrate that JPEG XT can encode HDR images at bit rates varying from 1:1 to 1:9 bit/pixel for estimated mean opinion score (MOS) values above 4:5 out of 5, which is considered as fully transparent in many applications. This corresponds to 23-times bit stream reduction compared to lossless OpenEXR PIZ compression. In the conclusions the authors [JXT6] state “The benchmarking showed that, in terms of predicting quality loss due to coding artifacts, simple metrics, such as PSNR, SNR, and MRSE computed in linear space are unsuitable for measuring perceptual quality of images compressed with JPEG XT, however, the prediction of these metrics improve when applied to the pixels converted in the perceptually uniform space. Also, HDR-VDP-2 provides the best performance as compared to other tested metrics.” This again confirms that that these objective metrics do not correlate with the subjective (perceptual) quality. JXT7 M. Shah, “Future of JPEG XT: Scrambling to enhance privacy and security”, M.S. Thesis, EE Dept., UTA, Arlington, TX, USA. http://www.uta.edu/faculty/krrao/dip click on courses and then click on EE5359 Scroll down and go to recent Theses/Project Title and click on Maitri Shah. JXT8 R. K. Mantiuk, T. Richter and A. Artusi, “ Fine-tuning JPEG-XT compression performance using large-scale objective quality testing”, IEEE ICIP, Phoenix, Arizona, Sept. 2016. Two publicly available (image datasets) used in [JXT6] Fairchild’s HDR photographic survey. http://rit-mcsl.org/fairchild/HDR.html HDR-eye dataset of HDR images. http://mmspg.ep.ch/hdr-eye The dataset contained scenes with architecture, landscapes, portraits, frames extracted from HDR video, as well as computer generated images. JXT9 P. Korshunov et al, “EPFL’s dataset of HDR images”’ 2015 (online), Available: http://mmsp.epfl.ch/hdr-eye JXT10 T. Richter, “JPEG on steroids: Common optimization techniques for JPEG image compression”, IEEE ICIP, Phoenix, AZ, Sept. 2016. JPEG SOFTWARE: JXT11 J. Aas: “Mozilla JPEG encoder project”, available online at https://github.com/mozilla/mozjpeg (See [JXT10]) JXT12 T. Richter, “Error bounds for HDR image coding with JPEGXT”, IEEE DCC, April 2017. Mozilla JPEG Encoder Project This project's goal is to reduce the size of JPEG files without reducing quality or compatibility with the vast majority of the world's deployed decoders. The idea is to reduce transfer times for JPEGs on the Web, thus reducing page load times. 'mozjpeg' is not intended to be a general JPEG library replacement. It makes tradeoffs that are intended to benefit Web use cases and focuses solely on improving encoding. It is best used as part of a Web encoding workflow. For a general JPEG library (e.g. your system libjpeg), especially if you care about decoding, we recommend libjpeg-turb libjpeg A complete implementation of 10918-1 (JPEG) coming from jpeg.org (the ISO group) with extensions for HDR currently discussed for standardization. JXT13 Jpegxt demo software T. Richter: “libjpeg: A complete implementation of 10918-1 (JPEG)”, available online at https://github.com/thorfdbg/libjpeg (See [JXT10]). Yüklə 1,18 Mb. Dostları ilə paylaş:
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