A New Frame Memory Compression Algorithm with DPCM and VLC in a 4×4 Block

Jin, Yongseok; Lee, Yongje; Lee, Hyuk-Jae

doi:10.1155/2009/629285

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…Meanwhile, there have been extensive efforts to reduce external memory bandwidth, so-called frame memory compression (FMC) [2][3][4]. But, additional information of each processing unit also has to be stored in internal frame memory; the amount of additional information could detract from the effectiveness of memory compression scheme.…”

Section: Related Workmentioning

confidence: 99%

A Bus Data Compression Method on a Phase-Based On-Chip Bus

Lee¹

2012

JSTS:Journal of Semiconductor Technology and Science

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Abstract-This paper provides a method for compression transmission of on-chip bus data. As the data traffic on on-chip buses is rapidly increasing with enlarged video resolutions, many video processor chips suffer from a lack of bus bandwidth and their IP cores have to wait for a longer time to get a bus grant. In multimedia data such as images and video, the adjacent data signals very often have little or no difference between them. Taking advantage of this point, this paper develops a simple bus data compression method to improve the chip performance and presents its hardware implementation. The method is applied to a Video Codec -1 (VC-1) decoder chip and reduces the processing time of one macro-block by 13.6% and 10.3% for SD and HD videos, respectively Index Terms-System-on-chip, on-chip bus, multimedia processor, VC-1 codec, video processor

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Section: Related Workmentioning

confidence: 99%

A Bus Data Compression Method on a Phase-Based On-Chip Bus

Lee¹

2012

JSTS:Journal of Semiconductor Technology and Science

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“…In particular, the power consumption issue is more critical for battery-powered mobile devices [6]. To solve these problems in mobile devices, the use of frame memory compression (FMC) based on the embedded compression (EC) schemes has gained attention [7]- [11]. EC-based FMC, which has low latency and low internal power consumption, is a very effective technique for solving external memory bandwidth issues and reducing power consumption by being installed between the processing unit and external memory.…”

Section: Introductionmentioning

confidence: 99%

RL-SPIHT: Reinforcement Learning-Based Adaptive Selection of Compression Ratios for 1-D SPIHT Algorithm

Shin

Kim

2021

IEEE Access

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In recent years, deep learning technologies have been actively used in various applications. In particular, networks trained using reinforcement learning (RL) are widely exploited for auxiliary tasks in various multimedia frameworks, including image restoration, image compression, and computer vision. Discrete wavelet transform (DWT) and set partitioning in hierarchical trees (SPIHT) are the representative lightweight compression methods that are most widely used for the purposes of frame memory compression and LCD overdrive. In precedent research, in order to improve the compression efficiency of DWT-SPIHT algorithms, the relative complexity of DWT coefficients is quantified, and when compressing DWT coefficients with the SPIHT algorithm, the compression ratio (CR) is adaptively allocated to the compression block according to the numerically expressed complexity. However, the SPIHT algorithm has the characteristic of resource limitation, resulting in the occurrence of remaining blocks, which cannot take advantage of allocating the adaptive CR. Moreover, since the equation expressing the block complexity that determines the CR of each block is obtained through machine learning-based linear regression, it lacks the capability to deal with a wide range of real-world images. To compensate for these drawbacks, this paper optimizes the compression efficiency of the 1-D DWT-SPIHT algorithm using the RL-based episodic auxiliary task. In detail, the proposed method optimally adjusts the proportion of CRs, which are adaptively selected for each block according to the DWT coefficient, through the episodic model trained with the RL algorithm. Consequently, the proposed method achieves an average improvement in peak signal to noise ratio (PSNR) of 2.18dB compared to the baseline 1-D DWT-SPIHT with the fixed compression ratio and 0.68dB compared to the precedent research.

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“…Even when SPIHT is not a standard, it is used in many video applications that use image compression with the embedded coding property [23][24][25][26] and medical imaging applications with SPIHT as a core. 8,20,27,28 One of the main drawbacks of SPIHT is the slow coding speed owing to the dynamic processing order that depends on the image contents.…”

Section: Introductionmentioning

confidence: 99%

“…8,20,27,28 One of the main drawbacks of SPIHT is the slow coding speed owing to the dynamic processing order that depends on the image contents. 23,[29][30][31] Efforts to improve the performance have been mostly concentrated in modifying the lists used to store the sets coordinates. For example, Pan et al 21 proposed a listless modified SPIHT by taking advantage of the lifting wavelet scheme and weighted the transform coefficients according to the human visual system.…”

Section: Introductionmentioning

confidence: 99%

Modified set partitioning in hierarchical trees algorithm based on hierarchical subbands

Domínguez

Villegas

Sánchez

2015

J. Electron. Imaging

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Abstract. This paper introduces a modified set partitioning in hierarchical trees (SPIHT) algorithm that reduces the number of comparison operations and, consequently, the execution time needed to encode an image as compared to the SPIHT algorithm. The threshold of each independent subband is calculated after applying the discrete wavelet transform to the image. Scanning of the sets inside the subbands is determined by the magnitude of the thresholds that establishes a hierarchical scanning not only for the set of coefficients with larger magnitude, but also for the subbands. The algorithm uses the set partitioning technique to sort the transform coefficients. Results show that the modified SPIHT significantly reduces the number of operations and the execution time without sacrificing visual quality and the PSNR of the recovered image. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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A New Frame Memory Compression Algorithm with DPCM and VLC in a 4×4 Block

Cited by 13 publications

References 18 publications

A Bus Data Compression Method on a Phase-Based On-Chip Bus

A Bus Data Compression Method on a Phase-Based On-Chip Bus

RL-SPIHT: Reinforcement Learning-Based Adaptive Selection of Compression Ratios for 1-D SPIHT Algorithm

Modified set partitioning in hierarchical trees algorithm based on hierarchical subbands

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