Dictionary-based Compression Method Research Articles

Survey of Compression of DNA Sequence

Compression of large collections of data can lead to improvements in retrieval times by offsetting the CPU decompression costs with the cost of seeking and retrieving data from disk. In this paper, the author has study the different compression method which can compress the large DNA sequence. In this paper, authors have explored the DNA compression method that is COMRAD, which is used to compare with the dictionary based compression method i.e. LZ77, LZ78, LZW and general purpose compression method RAY. In this, authors have analyzed which one algorithm is better to compress the large collection of the DNA Sequence. Compression table and the line graph show that which compression algorithm has a better compression ratio and the compression size. It also shows that which one has better compression and decompression time.

HCCMeshes: Hierarchical-Culling oriented Compact Meshes

Hierarchical culling is a key acceleration technique used to efficiently handle massive models for ray tracing, collision detection, etc. To support such hierarchical culling, bounding volume hierarchies (BVHs) combined with meshes are widely used. However, BVHs may require a very large amount of memory space, which can negate the benefits of using BVHs. To address this problem, we present a novel hierarchical-culling oriented compact mesh representation, HCCMesh, which tightly integrates a mesh and a BVH together. As an in-core representation of the HCCMesh, we propose an i-HCCMesh representation that provides an efficient random hierarchical traversal and high culling efficiency with a small runtime decompression overhead. To further reduce the storage requirement, the in-core representation is compressed to our out-of-core representation, o-HCCMesh, by using a simple dictionary-based compression method. At runtime, o-HCCMeshes are fetched from an external drive and decompressed to the i-HCCMeshes stored in main memory. The i-HCCMesh and o-HCCMesh show 3.6:1 and 10.4:1 compression ratios on average, compared to a naively compressed (e.g., quantized) mesh and BVH representation. We test the HCCMesh representations with ray tracing, collision detection, photon mapping, and non-photorealistic rendering. Because of the reduced data access time, a smaller working set size, and a low runtime decompression overhead, we can handle models ten times larger in commodity hardware without the expensive disk I/O thrashing. When we avoid the disk I/O thrashing using our representation, we can improve the runtime performances by up to two orders of magnitude over using a naively compressed representation.