High Compression Quality Research Articles

Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

When using the current method to compress the low frame rate video animation video, there is no frame rate compensation for the video image, which cannot eliminate the artifacts generated in the compression process, resulting in low definition, poor quality, and low compression efficiency of the compressed low frame rate video animation video. In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm. In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video animation using local convolution. According to the estimation results, the video frames are compensated to eliminate the artifacts of low frame rate video animation. After the frame rate compensation, the low frame rate video animation video is divided into blocks, the CS value of the image block is measured, the linear estimation of the image block is carried out by using the linear function model, and the compression of the low frame rate video animation video is completed according to the best linear estimation result. The experimental results show that the low frame rate video and animation video compressed by the proposed algorithm have high definition, high compression quality under different compression ratios, and high compression efficiency under different compression ratios.

VQ‐based compressive sensing with high compression quality

Natural image reconstruction based on compressive sensing (CS) has shown a promising performance in recent years. However, sometimes the restoration precision is not high enough. A novel CS algorithm using vector quantisation (VQ) error is proposed. First, the original image is compressed by VQ due to its extremely high compression ratio and strong ability to preserve details. Then compute the VQ error matrix and ignore the three least significant bits, which makes the error matrix much sparser. Next, to ensure a uniform distribution of sparsity of blocks, the error matrix is scrambled. Since the huge diversity among blocks has been largely reduced, they can be sensed with the same sensing matrix in space domain. At last, the reconstruction effect of the error matrix decides the total restoration performance. Experimental results have demonstrated the proposed method, at low measurement ratio, performs better in the aspects of perception and peak signal-to-noise ratio.

Stochastic Circuit Design and Performance Evaluation of Vector Quantization for Different Error Measures

Vector quantization (VQ) is a general data compression technique that has a scalable implementation complexity and potentially a high compression ratio. In this paper, a novel implementation of VQ using stochastic circuits is proposed and its performance is evaluated against conventional binary designs. The stochastic and binary designs are compared for the same compression quality, and the circuits are synthesized for an industrial 28-nm cell library. The effects of varying the sequence length of the stochastic representation are studied with respect to throughput per area (TPA) and energy per operation (EPO). The stochastic implementations are shown to have higher EPOs than the conventional binary implementations due to longer latencies. When a shorter encoding sequence with 512 bits is used to obtain a lower quality compression measured by the $L^{1}$ -norm, squared $L^{2}$ -norm, and third-law errors, the TPA ranges from 1.16 to 2.56 times than that of the binary implementation with the same compression quality. Thus, although the stochastic implementation underperforms for a high compression quality, it outperforms the conventional binary design in terms of TPA for a reduced compression quality. By exploiting the progressive precision feature of a stochastic circuit, a readily scalable processing quality can be attained by halting the computation after different numbers of clock cycles.

Ultrahigh Soliton Pulse Compression Through Liquid Crystal Photonic Crystal Fiber

In this paper, one-stage nematic liquid crystal photonic crystal fiber (NLC-PCF) soliton pulse compressor is proposed and analyzed using full-vectorial finite-element method. The suggested design has high birefringence due to the infiltration of the NLC material. Therefore, it can be used as a soliton pulse compressor for the two polarized modes. The reported NLC-PCF compressor has high compression factor ( $F_{C}$ ) of 10.25 with high compression quality factor ( $Q_{C}$ ) of 0.84 at the operating wavelength of 1.55 μm for the quasi-TE mode with short device length of 76 cm. However, the $F_{C}$ and $Q_{C}$ of the quasi TM mode is equal to 10.93 and 0.66, respectively, at the same device length. The pulse compression through higher order solitons is allowed through controlling the fiber dispersion and nonlinear effects. In addition, greater compression can be achieved with large numbers of soliton order.

A large ratio data compression method for pipeline inspection based on EMAT-generated guided wave

In order to meet the engineering requirement of gas pipeline inline inspection for cracking defects, a new ultrasonic guided wave (GW) method based on electromagnetic acoustic transducer (EMAT) has emerged. In consideration of the large amount of original inspection data and the limited capacity of the storing device, the research on rapid and large ratio data compression is needed. This paper proposes a new large ratio data compression method based on subsection adaptive method (SAM) and wavelet neural network (WNN) algorithm. The influences of parameter selection on the compression quality, speed and ratio and the method of enhancing the compression speed of this method is studied. The experiment results indicate that compared with the traditional WWN compression algorithm, the proposed method has higher compression quality and speed, and a large compression ratio of 110:1 is achieved by the proposed method. It is thus suitable for gas pipeline crack inline inspection based on EMAT-generated GW.

A Novel Method of Data Stream Clustering Based on Wavelet Timing Series Tree Synopsis

For the difficulty of obtaining cluster result fast and effectively under the limitations of bounded memory and time, this paper proposes a novel data stream clustering method based on wavelet timing series tree synopsis to solve the problem. The proposed method considers the attenuation characteristic of data stream, which combines the dynamic maintenance of wavelet coefficient and attenuation feature of wavelet coefficients of data stream, and can achieve approximate representation of data stream fragment information and dynamic maintenance of its synopsis structure. The proposed method employs wavelet timing series tree synopsis method to compress data stream fragment, then adopts two-phase density clustering algorithm to cluster. Detailed experiments show that the proposed method can get high compression quality, good space and time efficiency and good clustering results.

Compressing repeated content within large-scale remote sensing images

Large-scale remote sensing images, including both satellite and aerial photographs, are widely used to render terrain scenes in real-time geographic visualization systems. Such systems often require large memories in order to store fine terrain details and fast network speeds to transfer image data, if they are built as web applications. In this paper, we propose a progressive texture compression framework to reduce the memory and bandwidth cost by compressing repeated content within and among large-scale remote sensing images. Different from existing image factorization methods, our algorithm incrementally find similar regions in new images so that large-scale images can be more efficiently compressed over time. We further propose a descriptor, the Gray Split Rotate (GSR) descriptor, to accelerate the similarity search. The reconstruction quality is finally improved by compressing residual error maps using customized S3TC-like compression. Our experiment shows that even with the error maps, our system still has higher compression rate and higher compression quality than using S3TC alone, which is a typical compression solution in most existing visualization systems.

A wavelet-VQ system for real-time video compression

This paper presents a scheme and its Field Programmable Gate Array (FPGA) implementation for a system based on combining the bi-dimensional discrete wavelet transformation (2D-DWT) and vector quantization (VQ) for image compression. The 2D-DWT works in a non-separable fashion using a parallel filter structure with distributed control to compute two resolution levels. The wavelet coefficients of the higher frequency sub-bands are vector quantized using multi-resolution codebook and those of the lower frequency sub-band at level two are scalar quantized and entropy encoded. VQ is carried out by self organizing feature map (SOFM) neural nets working at the recall phase. Codebooks are quickly generated off-line using the same nets functioning at the training phase. The complete system, including the 2D-DWT, the multi-resolution codebook VQ, and the statistical encoder, was implemented on a Xilinx Virtex 4 FPGA and is capable of performing real-time compression for digital video when dealing with grayscale 512 × 512 pixels images. It offers high compression quality (PSNR values around 35 dB) and acceptable compression rate values (0.62 bpp).

VLSI Design of a High-Speed and Area-Efficient JPEG2000 Encoder

A high-speed VLSI design of an area-efficient JPEG2000 encoder is given. Recursive multilevel 2D discrete wavelet transform (DWT) architecture with dual buffers is proposed to reduce the wavelet coefficients memory to 1/4 tile size, prerate allocation is used to reduce the compressed code memory to 3/4 tile size. A highly pipelined and parallelism implementation of line-based 1-level DWT is proposed using two line-buffers in 5/3 wavelet type and its input speed is up to 2 samples/cycle; code block based address mapping in access wavelet coefficients memory, concurrent state variables generation and multiple parallel and pipeline coding methods are used in the bit plane encoder (BPE) which encodes on average at 40.5 M samples/s at 100 MHz with no memory used; the conditional two-symbol pipeline arithmetic encoder (AE) encodes at 1.3 symbols/cycle. Parallel units in BPE and buffer control between BPE and AE are optimally implemented with low cost without performance loss. Byte representation of rate-distortion slope used reaches a near optimal implementation of post-coding rate distortion in Tier2 with low cost. The compressed file generated by the encoder is fully compatible with ISO/IEC FCD15444-1. The encoder is verified on field-programmable gate array platform with a direct interface to digital video input with tile size 256 times 256 and code block size 32 times 16. The resulting input sampling rate is up to 58 M samples/s when Tier1 operates at 100 MHz. Difference of the peak signal-to-noise ratio of images compressed by our encoder and JasPer is less than 0.2 dB when the compression ratio is greater than 1 bps. Equivalent NAND2 gates synthesized are 90.6 K and on-chip RAM size is 626.75 kb. Unlike other designs the proposed design of JPEG2000 encoder has high compression quality as well as high speed and area-efficiency.