Reduction of Artefacts in JPEG-XR Compressed Images

Hua, Kai-Lung; Trang, Ho Thi; Srinivasan, Kathiravan; Chen, Yung-Yao; Chen, Chun‐Hao; Sharma, Vishal; Zomaya, Albert Y.

doi:10.3390/s19051214

Cited by 4 publications

(2 citation statements)

References 27 publications

(36 reference statements)

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“…By knowing the size, shape and spatial location of the tumor, the process of treating the tumor might be improved. The future work could include the implementation of advanced artificial intelligence methodologies for early, efficient, and real-time diagnosis of malignant brain tumors [58][59][60][61][62][63][64][65].…”

Section: Discussionmentioning

confidence: 99%

An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

et al. 2020

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Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work, Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for different k. Then, the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness function. The clustered boundaries of the tissue classes further enhanced by morphological operations. The FC technique is hybridized with the standard image post-processing techniques such as marker controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets (DRLS). This procedure is implemented on renowned BRATS challenge dataset of different modalities and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of the slices is performed using the metadata information present in the DICOM header. The validation of the segmentation procedures against the ground truth images authorizes that the segmented objects of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity coefficients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D visualization and tumor volume estimation are done using accessible DICOM information.

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Section: Discussionmentioning

confidence: 99%

An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

et al. 2020

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“…During the encoding process, the quantization parameter greater than one makes the model lossy compression, and the output image contains degradation artifacts. These artifacts are presented by the nonlinearity of transforms used by JPEG XR [13].…”

Section: Jpeg Xr Compressionmentioning

confidence: 99%

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

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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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