@techreport{oai:ipsj.ixsq.nii.ac.jp:00174806,
 author = {船坂，峻慈 and 中野，浩嗣 and 伊藤，靖朗 and Shunji, Funasaka and Koji, Nakano and Yasuaki, Ito},
 issue = {6},
 month = {Sep},
 note = {データ圧縮はコンピュータエンジニアリングの分野で非常に重要である．しかし,多くの可逆圧縮と展開アルゴリズムは並列化が非常に難しい.本論文では Light Loss - Less (LLL) 圧縮と呼ぶ,新しい可逆圧縮法を提案する．この圧縮法の展開アルゴリズムは高い並列化が可能であり GPU を用いて非常に高速に処理することができる．データ展開は圧縮と比較して何度も行うためにこの圧縮法は多くのアプリケーションで応用できる．我々は LLL 展開の並列アルゴリズムを提案し GeForce GTX 1080 GPU に実装した.GPU を用いた LLL 展開の実効速度を Core i7- 4790 への逐次 CPU 実装と比較し 91.1-176 倍の高速化を達成した．また，よく知られている圧縮手法である LZSS と LZW との比較も行う．提案手法は圧縮率は同程度である一方で LZSS 展開の GPU 実装と比較して 4.30-14.1 倍,LZW 展開の GPU 実装と比較して 2.49-9.13 倍の高速化を達成した．, There is no doubt that data compression is very important in computer engineering. However, most lossless data compression and decompression algorithms are very hard to parallelize, because they use dictionaries updated sequentially. The main contribution of this paper is to present a new lossless data compression method that we call Light Loss-Less (LLL) compression. It is designed so that decompression can be highly parallelized and run very efficiently on the GPU. This makes sense for many applications in which compressed data is read and decompressed many times and decompression performed more frequently than compression. We show optimal sequential and parallel algorithms for LLL decompression and implement them to run on Core i7-4790 CPU and GeForce GTX  1080 GPU, respectively. To show the potentiality of LLL compression method, we have evaluated the running time using five images and compared with well-known compression methods LZW and LZSS. Our GPU implementation of LLL decompression runs 91.1-176 times faster than the CPU implementation. Also, the running time on the GPU of our experiments show that LLL decompression is 2.49-9.13 times faster than LZW decompression and 4.30-14.1 times faster that LZSS decompression, although their compression ratios are comparable.},
 title = {GPUのための可逆データ圧縮アルゴリズム},
 year = {2016}
}