The traditional image compressors, e.g., BPG and H.266, have achieved great image and video compression quality. Recently, Convolutional Neural Network has been used widely in image compression. We proposed an attention-based convolutional neural network for low bit-rate compression to post-process the output of traditional image compression decoder. Across the experimental results on validation sets, the post-processing module trained by MAE and MS-SSIM losses yields the highest PSNR of 32.10 on average at the bit-rate of 0.15.
JPEG is a widely used image format using 2D Fourier Discrete Cosine (DCT) Transform. It is also the foundation of most popular H.264 @cite_15 video compression format. However, JPEG 2000 uses the wavelet transform to beat up JPEG for its higher quality in the same level of Bit Per Pixel (BPP). However, the lack of its application and slow encoding and decoding speed hinder its popularity. Google presented WebP in 2010 in order to substitute JPEG or PNG on the internet. The Predicting Module in the MacroBlocking of WebP sends the predicted output to the DCT transform, thus compressing the image in a more effective way. People tend to find a better way of image compression from video compression techniques. WebP can be seen as the key-frame compression of WebM @cite_17 . BPG is derived from HEVC @cite_2 (a.k.a the second part of H.265). It gives a higher dynamic range and a better compression ratio. H.266, i.e., Versatile Video Coding (VVC) @cite_13 proposed by JVET group, is beyond H.265 and desire to have a preferable performance than any other traditional image video compression methods.
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JPEG 2000 is an image coding system based on the discrete wavelet transform. Unfortunately, there exist several major issues with the effective implementation of the codec. For high resolution data decomposed by a separable transform, immensely many CPU cache misses occur. Following the procedure as defined in the standard, the coefficients of a single resolution appears all at once. Consequently, the entropy coder (EBCOT) needs to once again return to the data already touched. We present a software architecture designed for JPEG 2000 coders. The proposed method employs a strip-based data processing technique while it performs a single-pass multi-scale wavelet transform. The overall compression chain is driven by incoming data while the fragments of the resulting bitstream can be produced immediately after loading the corresponding data and additionally in parallel. The method is friendly to the CPU cache and can nicely exploit the SIMD extensions. The JPEG2000 standard uses the 2D Discrete Wavelet Transform (2D DWT), while the JPEG standard uses the 2D Discrete Cosine Transform (DCT). However, the 2D DWT has higher computational requirements than the 2D DCT and consumes a significant part of the total JPEG2000 encoding time. One way to improve the performance of the 2D DWT is using parallel techniques on an SIMD-enhanced architecture. In this paper, we apply data-level parallelism technique to exploit available parallelism of the 2D DWT. We focus on the two algorithms to traverse an image to implement the 2D Discrete Wavelet Transform (DWT), namely Row-Column Wavelet Transform (RCWT) and Line-Based Wavelet Transform (LBWT). Our experimental results show that the SIMD implementation of the LBWT algorithm is more complicated than the SIMD implementation of the RCWT algorithm, while the former algorithm is 1.60 times faster than the latter algorithm for an image of size 4096 × 4096. The discrete wavelet transform (DWT), the technology at the heart of the JPEG 2000 image compression system, operates on user-definable tiles of the image, as opposed to fixed-size blocks of the image as does the discrete cosine transform (DCT) used in JPEG. This difference reduces artificial blocking effects but can severely stress the memory system. We examine the interaction of the DWT and the memory hierarchy, modify the structure of the DWT computation and the layout of the image data to improve cache and translation lookaside buffer (TLB) locality, and demonstrate significant performance improvements of the DWT over a baseline implementation. Our optimized DWT implementation exhibits speedups of up to 4 spl times over the DWT in a JPEG 2000 reference implementation. A joint ISO CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international compression standard for continuous-tone still images, both grayscale and color. JPEG's proposed standard aims to be generic, to support a wide variety of applications for continuous-tone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each with various modes of operation. A DCT (discrete cosine transform)-based method is specified for 'lossy' compression, and a predictive method for 'lossless' compression. JPEG features a simple lossy technique known as the Baseline method, a subset of the other DCT-based modes of operation. The Baseline method has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications. The author provides an overview of the JPEG standard, and focuses in detail on the Baseline method. >