JPEG XR an Image Coding Standard

Jadhav, Savita S.; Jadhav, Sandeep K.

doi:10.7763/ijcee.2012.v4.465

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…However, JPEG XR was the fastest encoder of all the codecs that were evaluated by 9 seconds on average and used 487MB less RAM and 822MB less video memory on average than JPEG 2000 (Figures 6-9). These findings may be partially attributed to JPEG XR's use of a form of adaptive Huffman entropy coding [42], [43]. Though often suboptimal in terms of compression, Huffman coders are less complex and faster than arithmetic coders [39], [44].…”

Section: Jpeg 2000 and Jpeg Xrmentioning

confidence: 99%

Benchmarking Lossless Still Image Codecs: Perspectives on Selected Compression Standards From 1992 through 2022

Bennett

2023

archiving

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As complementary technologies evolve, data compression continues to be a foundational aspect of growing digital collections. In this study, selected lossless still image codecs from 1992 through 2022 were benchmarked across a variety of efficiency and performance measures using reference images from cultural heritage. Additionally, entropy estimates were calculated by source image to assist in characterizing image information and evaluating encoder efficiency against assessed feasible compression limits. Encoder designs and compression techniques were also examined in the context of the study's measured results.

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Section: Jpeg 2000 and Jpeg Xrmentioning

confidence: 99%

Benchmarking Lossless Still Image Codecs: Perspectives on Selected Compression Standards From 1992 through 2022

Bennett

2023

archiving

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“…and each precinct is divided into four packets. Packet 0 contains bands (0, 1, 2), (3,4,5), and (6, 7, 8). Packet 1 contains bands (9,10,11).…”

Section: Image Structurementioning

confidence: 99%

“…Moreover, the Joint Photographic Experts Group has been promoting the development of image compression standards for nearly 30 years. The group has continuously introduced JPEG-LS, JPEG 2000 [4], JPEG XR [5], and other better codec standards, but JPEG still dominates the market, which shows that the performance of JPEG can meet the demand compared to other more complex codec standards. Low complexity and easy implementation are more important [6][7].…”

Section: Introductionmentioning

confidence: 99%

Implementation of JPEG XS entropy encoding and decoding on FPGA

Tian,

He,

Wang

et al. 2023

Preprint

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JPEG XS is the latest international standard for shallow compression fields launched by the International Organization for Standardization (ISO). The coding standard was officially released in 2019. The JPEG XS standard can be encoded and decoded on different devices, but there is no research on the implementation of JPEG XS entropy codec on FPGAs. This paper briefly introduces JPEG XS encoding, proposes a modular design scheme of encoder and decoder on FPGA for the entropy encoding and decoding part, and parallelizes the algorithm in JPEG XS coding standard according to the characteristics of FPGA parallelization processing, mainly including low-latency optimization design, storage space optimization design. So that the coding speed can reach 4 coefficients/clock and the decoding speed reach 2 coefficients/clock, which reduces the encoding and decoding time by 75%. The maximum clock frequency of the entropy encoder is about 222.6MHz, and the maximum clock frequency of the entropy decoder is about 127MHz. The design and implementation of the FPGA-based JPEG XS entropy encoding and decoding algorithm is of great significance and provides ideas for the subsequent implementation and optimization of the entire JPEG XS standard on FPGAs. This work is the first in the world to propose the design and implementation of an algorithm that can implement the JPEG XS entropy encoding and decoding process on FPGA. It creates the possibility for the effective application of JPEG XS standard in more media.

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“…The experimental research [14] has proven that JPEG XR and JPEG2000 demonstrate higher visual quality than JPEG compression under the high compression ratio. The JPEG2000 compression standard has a more complicated algorithm than the JPEG standard [15]. Compared to the JPEG2000 compression standard, JPEG XR has lower complexity and produces similar PSNR quality at the same bit rate.…”

Section: Jpeg Xr Compressionmentioning

confidence: 99%

Performance analysis of JPEG XR with deep learning-based image super-resolution

Min

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The demand for efficient high-level image and video codec compression has widely increased. Conventional image compression methods such as JPEG XR use a high quantization parameter (QP) to produce a highly compressed file for any given image. However, higher QP has unpleasing artifacts that lead to perceptual quality degradation. A feasible solution to tackle this limitation is to reduce the high-resolution image size by downsampling it before encoding it with JPEG XR. Then, the super-resolution algorithm is applied to the resultant low-resolution image to reconstruct the high-resolution result. In this research, we downsample the input image before JPEG XR. Then, we investigate the performance of integrating a newly retrained deep learning-based FSRCNN super-resolution (SR) with JPEG XR in terms of quality and compressed file size. According to the experimental results, the experimental results show that the proposed method outperforms JPEG XR compression by shrinking the size of the encoded file by an average of 557 kB for scale two and 756 kB for scale four. The fusion of the newly trained model with JPEG XR compression can achieve higher performance than JPEG XR compression in compressing the file, around 66% for scale two and 89% for scale four. The proposed method also produces a small, compressed file size for high compression and achieves better visual quality than JPEG XR compression, JPEG XR with the bicubic method, and JPEG XR with FSRCNN.

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JPEG XR an Image Coding Standard

Cited by 8 publications

References 3 publications

Benchmarking Lossless Still Image Codecs: Perspectives on Selected Compression Standards From 1992 through 2022

Benchmarking Lossless Still Image Codecs: Perspectives on Selected Compression Standards From 1992 through 2022

Implementation of JPEG XS entropy encoding and decoding on FPGA

Performance analysis of JPEG XR with deep learning-based image super-resolution

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