Medical image compression using advanced coding technique

Sridhar, K.; Prasad, K. Siva

doi:10.1109/icosp.2008.4697570

Cited by 3 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to its use in video coding, CAVLC has many interesting applications and great possibilities in other areas of video and image compression, like medical image compression [20,30,37].…”

Section: Cavlc Applicationsmentioning

confidence: 99%

“…Sridhar et al [37] proposed an advanced medical image compression technique based on integer DCT (Digital Cosine Transform), SPIHT (Set Partitioning In Hierarchical Trees) and CAVLC. Simulations on different medical images (including CT skull, angiogram and MR images) showed better results compared to JPEG and JPEG2000 schemes.…”

Section: Cavlc Applicationsmentioning

confidence: 99%

“…In that case, many adaptations of CAVLC proposed in different fields, like data encryption [19,[40][41][42] and information hiding [16,17,45,46], can be applied. Second, implementations of CAVLC on GPU can be reused and easily adapted in the development of a great variety of GPGPU compression systems for encoding both images and videos in formats other than H.264, like medical images [20,30,37]. This is not possible with hardware implementations of CAVLC, as they are non-separable components of hardware H.264 encoders.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CAVLCU: an efficient GPU-based implementation of CAVLC

et al. 2021

View full text Add to dashboard Cite

CAVLC (Context-Adaptive Variable Length Coding) is a high-performance entropy method for video and image compression. It is the most commonly used entropy method in the video standard H.264. In recent years, several hardware accelerators for CAVLC have been designed. In contrast, high-performance software implementations of CAVLC (e.g., GPU-based) are scarce. A high-performance GPU-based implementation of CAVLC is desirable in several scenarios. On the one hand, it can be exploited as the entropy component in GPU-based H.264 encoders, which are a very suitable solution when GPU built-in H.264 hardware encoders lack certain necessary functionality, such as data encryption and information hiding. On the other hand, a GPU-based implementation of CAVLC can be reused in a wide variety of GPU-based compression systems for encoding images and videos in formats other than H.264, such as medical images. This is not possible with hardware implementations of CAVLC, as they are non-separable components of hardware H.264 encoders. In this paper, we present CAVLCU, an efficient implementation of CAVLC on GPU, which is based on four key ideas. First, we use only one kernel to avoid the long latency global memory accesses required to transmit intermediate results among different kernels, and the costly launches and terminations of additional kernels. Second, we apply an efficient synchronization mechanism for thread-blocks (In this paper, to prevent confusion, a block of pixels of a frame will be referred to as simply block and a GPU thread block as thread-block.) that process adjacent frame regions (in horizontal and vertical dimensions) to share results in global memory space. Third, we exploit fully the available global memory bandwidth by using vectorized loads to move directly the quantized transform coefficients to registers. Fourth, we use register tiling to implement the zigzag sorting, thus obtaining high instruction-level parallelism. An exhaustive experimental evaluation showed that our approach is between 2.5$$\times$$ × and 5.4$$\times$$ × faster than the only state-of-the-art GPU-based implementation of CAVLC.

show abstract

Section: Cavlc Applicationsmentioning

confidence: 99%

Section: Cavlc Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CAVLCU: an efficient GPU-based implementation of CAVLC

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Sridhar and Prasad [19] combined the integer DCT-based SPIHT algorithm with context adaptive variable length coding (CAVLC) to encode important coefficients of medical images. Then, only these important coefficients were transmitted instead of transmitting the entire image data, thereby achieving compression effect.…”

Section: B Non-roi Codingmentioning

confidence: 99%

A Fast Fractal Based Compression for MRI Images

et al. 2019

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI), which assists doctors in determining clinical staging and expected surgical range, has high medical value. A large number of MRI images require a large amount of storage space and the transmission bandwidth of the PACS system in offline storage and remote diagnosis. Therefore, high-quality compression of MRI images is very research-oriented. Current compression methods for MRI images with high compression ratio cause loss of information on lesions, leading to misdiagnosis; compression methods for MRI images with low compression ratio does not achieve the desired effect. Therefore, a fast fractal-based compression algorithm for MRI images is proposed in this paper. First, three-dimensional (3D) MRI images are converted into a two-dimensional (2D) image sequence, which facilitates the image sequence based on the fractal compression method. Then, range and domain blocks are classified according to the inherent spatiotemporal similarity of 3D objects. By using self-similarity, the number of blocks in the matching pool is reduced to improve the matching speed of the proposed method. Finally, a residual compensation mechanism is introduced to achieve compression of MRI images with high decompression quality. The experimental results show that compression speed is improved by 2-3 times, and the PSNR is improved by nearly 10. It indicates the proposed algorithm is effective and solves the contradiction between high compression ratio and high quality of MRI medical images.

show abstract