Design and high-performance hardware architecture for image coding using block-lifting-based quaternionic paraunitary filter banks

In this paper, we have introduced a generalized block-lifting structure using the 2-D CORDIC algorithm as a block of 4-band linear phase paraunitary filter banks (LP PUFB) based on the quaternionic algebra (Q-PUFB) for the lossy-to-lossless image coding. A bank Q-PUFB based on the 2-D CORDIC block-lifting structure reduces the number of rounding operations and has a regular layout. Since the block-lifting structures with rounding operations can implement the integer-to-integer transform (Q-PUFB). The parallel-pipelined efficient architecture (P2E_Q-PUFB) has been proposed. The low latency separable image processing is implemented in the given architecture

Pipelined block-lifting-based embedded processor for multiplying quaternions using distributed arithmetic

This paper presents a systematic design of the of the integer-to-integer invertible quaternionic multiplier based on the block-lifting structure and pipelined embedded processor of the given multiplier using distributed arithmetic (DA) as a block of M-band linear phase paraunitary filter banks (LP PUFB) based on the quaternionic algebra (Q-PUFB) for the lossy-to-lossless image coding. A bank Q-PUFB based on the DA block-lifting structure reduces the number of rounding operations and has a regular layout. Since the block-lifting structures with rounding operations can implement the integer-to-integer transform (Q-PUFB)

Work design improvement at Miroad Rubber Industries Sdn. Bhd.

Erul Food Industries known as Salaiport Industry is a family-owned company and was established on July 2017. Salaiport Industry apparently moved to a new place at Pedas, Negeri Sembilan. Previously, Salaiport Industry operated in-house located at Pagoh, Johor. This small company major business is producing frozen smoked beef, smoked quail, smoke catfish and smoked duck. The main frozen product is smoked beef. The frozen smoked meat produced by Salaiport Industry is depending on customer demands. Usually the company produce 40 kg to 60 kg a day and operated between for four days until five days. Therefore, the company produce approximately around 80 kg to 120 kg per week. The company usually take 2 days for 1 complete cycle for the production as the first day the company will only receive the meat from the supplier and freeze the meat for use of tomorrow

M-Channel Fast Hartley Transform Based Integer DCT for Lossy-to-Lossless Image Coding

This paper presents an M-channel (M=2n (n ∈ N)) integer discrete cosine transforms (IntDCTs) based on fast Hartley transform (FHT) for lossy-to-lossless image coding which has image quality scalability from lossy data to lossless data. Many IntDCTs with lifting structures have already been presented to achieve lossy-to-lossless image coding. Recently, an IntDCT based on direct-lifting of DCT/IDCT, which means direct use of DCT and inverse DCT (IDCT) to lifting blocks, has been proposed. Although the IntDCT shows more efficient coding performance than any conventional IntDCT, it entails many computational costs due to an extra information that is a key point to realize its direct-lifting structure. On the other hand, the almost conventional IntDCTs without an extra information cannot be easily expanded to a larger size than the standard size M=8, or the conventional IntDCT should be improved for efficient coding performance even if it realizes an arbitrary size. The proposed IntDCT does not need any extra information, can be applied to size M=2n for arbitrary n, and shows better coding performance than the conventional IntDCTs without any extra information by applying the direct-lifting to the pre- and post-processing block of DCT. Moreover, the proposed IntDCT is implemented with a half of the computational cost of the IntDCT based on direct-lifting of DCT/IDCT even though it shows the best coding performance

Subband coding for image data archiving

The use of subband coding on image data is discussed. An overview of subband coding is given. Advantages of subbanding for browsing and progressive resolution are presented. Implementations for lossless and lossy coding are discussed. Algorithm considerations and simple implementations of subband systems are given

Learning Convolutional Networks for Content-weighted Image Compression

Lossy image compression is generally formulated as a joint rate-distortion optimization to learn encoder, quantizer, and decoder. However, the quantizer is non-differentiable, and discrete entropy estimation usually is required for rate control. These make it very challenging to develop a convolutional network (CNN)-based image compression system. In this paper, motivated by that the local information content is spatially variant in an image, we suggest that the bit rate of the different parts of the image should be adapted to local content. And the content aware bit rate is allocated under the guidance of a content-weighted importance map. Thus, the sum of the importance map can serve as a continuous alternative of discrete entropy estimation to control compression rate. And binarizer is adopted to quantize the output of encoder due to the binarization scheme is also directly defined by the importance map. Furthermore, a proxy function is introduced for binary operation in backward propagation to make it differentiable. Therefore, the encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner by using a subset of the ImageNet database. In low bit rate image compression, experiments show that our system significantly outperforms JPEG and JPEG 2000 by structural similarity (SSIM) index, and can produce the much better visual result with sharp edges, rich textures, and fewer artifacts