Area and power efficient DCT architecture for image compression
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RESEARCH
Open Access
Area and power efficient DCT architecture for image compression Vaithiyanathan Dhandapani* and Seshasayanan Ramachandran
Abstract The discrete cosine transform (DCT) is one of the major components in image and video compression systems. The final output of these systems is interpreted by the human visual system (HVS), which is not perfect. The limited perception of human visualization allows the algorithm to be numerically approximate rather than exact. In this paper, we propose a new matrix for discrete cosine transform. The proposed 8 × 8 transformation matrix contains only zeros and ones which requires only adders, thus avoiding the need for multiplication and shift operations. The new class of transform requires only 12 additions, which highly reduces the computational complexity and achieves a performance in image compression that is comparable to that of the existing approximated DCT. Another important aspect of the proposed transform is that it provides an efficient area and power optimization while implementing in hardware. To ensure the versatility of the proposal and to further evaluate the performance and correctness of the structure in terms of speed, area, and power consumption, the model is implemented on Xilinx Virtex 7 field programmable gate array (FPGA) device and synthesized with Cadence® RTL Compiler® using UMC 90 nm standard cell library. The analysis obtained from the implementation indicates that the proposed structure is superior to the existing approximation techniques with a 30% reduction in power and 12% reduction in area. Keywords: Discrete cosine transform (DCT); Multiplication-free transform; Low complexity; FPGA implementation; Image compression; VLSI architecture
1 Introduction Discrete cosine transform (DCT) [1] has become one of the basic tools in signal and image processing; the popularity of which is mainly due to its good energy compaction properties. In particular, DCT is the best substitute for the Karhunen-Loeve Transform (KLT), which is considered to be statistically optimal for energy concentration [2,3], whereas the discrete cosine transform is suboptimal. The KLT is data dependent and requires more computation compared to the DCT. Due to this fact, discrete cosine transform is the finest substitute for the KLT. Indeed, DCT has found applications in many image and video compression standard such as JPEG [4], MPEG-1 [5], MPEG-2 [6], H.261 [7], H.263 [8], and H.264/AVC [9,10]. During the JPEG process, an image is divided into several 8 × 8 blocks and then the two-dimensional discrete cosine transform
(2-D DCT) is applied for encoding each block. The two-dimensional DCT of order N × N is defined as π ð2i þ 1Þu T DCT ðu; vÞ ¼ αðuÞαðvÞ X ði; jÞ cos 2N i¼0 j¼0 π ð2j þ 1Þv cos for 0≤i; j; u; v≤N−1 2N N −1 X N −1 X
ð1Þ Where 8 rffiffiffiffi 9 1 > > > < = for u; v ¼ 0 > N ffiffiffiffi r αðuÞ ¼ αðvÞ ¼ > > 2 > : ; otherwise > N In general, the floating point DCT decorrelates the data being transformed so that most of its energy is packed in the low-frequency regio
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