Manipulation and compositing of MC-DCT compressed video

PurposeCone‐beam computerized tomography (CBCT) is routinely performed for verification of patient position in radiotherapy. It produced a large amount of data which require a method to compress them for efficient storage. In this study three video compression algorithms were introduced and their performance was evaluated based on real patient data.Materials and methodsAt first CBCT images in multiple sets of a patient were transferred from reconstruction workstation or exported from treatment planning system. Then CBCT images were sorted according to imaging time (time‐prioritized sequence) or imaging location (location‐prioritized sequence). Next, this sequence was processed by a video compression algorithm and resulted in a movie. Three representative video compression algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG‐4) were employed and their compression performance was evaluated based on the CBCT data of 30 patients.ResultsAmong three video compression algorithms, Motion JPEG 2000 has the least compression ratio since it is a lossless compression algorithm. Motion JPEG AVI and MPEG‐4 have higher compression ratios than Motion JPEG 2000 but come with certain image losses. For MPEG‐4, location‐prioritized sequences show higher compression ratio than time‐prioritized sequences. Based on the results achieved on the clinical target verification application, the registration accuracy of CBCT after decompression was comparable to that of the original CBCT.ConclusionsVideo compression algorithms could provide a higher compression ratio comparing to static image compression algorithm. Although the loss of CBCT image due to compression its impact on registration accuracy of patient positioning is almost negligible. Video compression method is an effective way to substantially reduce the size of CBCT images for storage.

Three dimensional (3D) video is attracting a lot of attention as a new multimedia representation method. 3D video is a sequence of 3D models (frames) that consist of varying vertices and connectivity. In conventional 2D video compression algorithms, motion compensation (MC) using block matching algorithm is frequently employed to reduce redundancy between consecutive frames. However, there is no such technology for 3D video so far. Therefore, in this paper, we have developed an extended block matching algorithm (EMBA) to reduce temporal redundancy of geometry information of 3D video by extending the idea of 2D block matching to 3D space. In our EBMA, a cubic block is used as a matching unit and, MC is achieved efficiently by matching the mean normal vectors of the sub-blocks, which turned out to be sub-optimal by our experiments. The residual information is further transformed by discrete cosine transform (DCT) and then encoded. The extracted motion vectors are also entropy encoded. As a result of our experiments, compression ratio ranging from 10% to 18% of the original 3D video data has been achieved.

The new demands for high-definition and high resolution video applications are pushing the development of new techniques in the video coding area. Motion estimation (ME) is one of the key elements in video coding standard which eliminates the temporal redundancies between successive frames. In the recent international video coding standards, sub-pixel ME is proposed for its excellent coding performance. DCT (Discrete Cosine Transform) has been widely used for digital signal processing and multimedia applications, especially video codec. In regards to interpolation for motion estimation and compensation, the conventional HEVC employs 8-tap and 4-tap filters for luma and chroma samples respectively. Coefficients in such filters are determined by DCT. This paper presents and implements the DCT-based Quarter-pixel Interpolation filter for motion estimation and compression of HEVC standard using Matlab.

The wavelet transformation is leading and widely used technology in transform coding. Many coding algorithms are designed for this transformation based on its unique structure, such as EZW, SPIHT and SPECK. The correlation between each subbands naturally generates a special tree structure in the whole image. With bitplane and entropy coding technique, the compression ratio can be achieved at a very high value. In this thesis, we focus on the traditional discrete cosine transform (DCT) to design our compression system. After analyzing the performance of the SPIHT algorithm, we found that, the coefficients' arrangement in DCT still has the features similar to these in wavelet transform and these features are vital to maximize the performance of SPIHT algorithm. For realistic implementation, a large hyperspectral data cube must be tiled into small size of code cubes for compression to achieve a fast compression speed. In JPEG standard, two applicable block sizes are give, which are 8×8 and 16×16 blocks. In our system, we extend these blocks into three-dimensional cube for hyperspectral image as 8×8×8 and 16×16×16 cubes for testing. To enhance the compression performance, PCRD algorithm is also applied in our system. Because the values in spectrum direction share very similar trajectory for each pixel, the power of several continuous bands is predictable. In this way, we optimized the PCRD algorithm for our system, and the truncation points can be chosen without calculation which saves time. Three AVIRIS hyperspectral data sets are tested. For DCT based compression, each image cube was tiled into sizes of 8×8×8 and 16×16×16 cubes for transformation and compression independently. For DWT based compression in small code cube setting, each image cube was tiled into sizes of 32×32×32 and 16×16×16 with a DWT decomposition level of five and four. For DWT compression in large code cube setting, the transformation (five levels of DWT) and compression algorithm was performed on the whole image cube in a size of 448×448×224. Results showed that, the DCT based compression with 16×16×16 code cube size has the best performance for lossy hyperspectral image compression and the bitplane arrangement is more effective for SPIHT algorithm.

The compressed domain video watermarking has been less expensive since complete decoding and then re-encoding is not required for watermark embedding. Handling synchronization error due to embedding is a challenging task, especially, due to motion compensation. In this paper, we propose an embedding scheme that can minimize desynchronization in the watermark extraction process at the decoder for embedding in compressed videos with better visual quality. Experimental results show the effectiveness of the proposed scheme.

compressed domain processing, transcoding, video editing, MPEG, splicing, reverse play, frame rate conversion, interlace to progressive, motion vector resampling Video compression algorithms are being used to compress digital video for a wide variety of applications, including video delivery over the Internet, advanced television broadcasting, video streaming, video conferencing, and video storage and editing. The impressive performance of modern compression algorithms, combined with the growing availability of video encoders and decoders and low-cost computers, storage devices, and networking equipment, makes it evident that between video capture and video playback, video will be handled in compressed video form. The resulting end-toend compressed digital video systems motivate the need to develop efficient algorithms for handling compressed digital video.

The Discrete Cosine Transform (DCT) is largely used for image and video compression in standards like JPEG and MPEG2. Its simplicity coupled with the fact that it can be computed faster than the Discrete Wavelet Transform (DWT) makes it an attractive option for image and video compression. Consequently a lot of research is in progress to determine novel architectures and algorithms for faster DCT computation having high throughput so that it can even be used for real time applications. For example, when transmitting and receiving videos, which have been compressed using MPEG2, one would like to receive the video, without any buffering delay. In other words, one requires such an architecture, which provides the DCTQ coeffecients at a consistent rate, with very low latency between two coeffecients. As such, a highly parallel and pipelined architecture employing 57 pipeline stages and using fast multipliers and adders is proposed in this work. The proposed DCT Processor is implemented in Spartan 3E FPGA.

Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the high- performance of discrete wavelet transform (DWT). It is time- consuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. Index Terms—Content based image retrieval (CBIR), compressed domain, discrete cosine transform (DCT), discrete wavelet transform (DWT), color features, texture features

Web user to avoid waiting to download a large file before seeing the video or hearing the sound. Instead, the media is sent in a continuous stream and is played as it arrives. The compressions of the video allow it to be played simultaneously. Depending on your choice of media player and the file's compression rate, the time it takes for the video to stream may vary. The file is hosted on a server which can multi-cast the same file to multiple users at the same time. When you click on the on the video it is partially loaded before you can access it. This is known as buffering. Now let's take a look on how to cut down on buffering time, stop slow streaming video and eliminate choppy PC video playback. Video has been an important media for communications and entertainment for many decades. Initially video was captured and transmitted in analog form. The advent of digital integrated circuits and computers lead to the digitization of video, and digital video enabled a revolution in the compression and communication of video. This Paper introduces the proposed architecture and algorithm for video streaming.

An image registration technique for compressed video such as motion JPEG or the I picture of MPEG based on the matching of DCT (Discrete Cosine Transform) coefficients is investigated in this research. Several simple features such as the DC value and a couple of low-frequency AC coefficients in the DCT domain are first extracted to indicate the edge strength and orientation inside each block for the image alignment purpose. Next, we conduct a coarse-level image segmentation task to filter out irrelevant regions. Then, for the regions of interest, we perform a more detail analysis to get the edge map. Finally, the alignment parameters are determined based on the information contained by the edge map. It is shown by experimental results that the proposed method reduces the computational cost of image registration dramatically as compared with the pixel domain registration technique while achieving certain quality of composition.

Huge number of images is available on the internet. Efficient and effective retrieval system is needed to retrieve these images by the contents or features of the images like color, texture and shape. This system is called content based image retrieval (CBIR). Conventionally features are extracted from images in pixel domain. But at present almost all images are represented in compressed form using DCT (Discrete Cosine Transformation) blocks transformation. Some critical information is removed in compression and only perceptual information is left which has significant attraction for information retrieval in compressed domain. In this paper we study the problem that how to retrieve perceptual information in compressed domain JPEG such that to improve image retrieval. Our approach is based on quantized histogram statistical texture features in DCT blocks. We show that to get best image retrieval performance by extracting the statistical texture features of quantized histogram in DCT blocks using JPEG compressed format images. Experiments on the Corel animal database using the proposed approach, give results which show that the statistical texture features of histogram are robust in retrieval of images. This shows that texture features in local compression is a significant step for effective image retrieval.

Objectives. The widespread use of systems for capturing light fields is due to the high quality of the reproduced image. This type of capture, although qualitatively superior to traditional methods to capturing volumetric images, generates a huge amount of data needed to reconstruct the original captured 4D light field. The purpose of the work is to consider traditional and extended to four-dimensional image compression algorithms, to perform a comparative analysis and determine the most suitable.Methods. Mathematical methods of signal processing and methods of statistical analysis are used.Results. Algorithms are compared and analyzed in relation to the compression of four-dimensional light fields using the PSNR metric. The selected evaluation criterion is affected not only by the dimension of the compression algorithm, but also by the distance of the baseline of the capture setting, since the difference between images increases with the distance between the optical centers of each camera matrix. Thus, for installations consisting of an array of machine vision cameras located on racks and placed in a room, the obvious choice would be to use conventional image compression methods. Furthermore, based on the assessment of the arbitrariness of video compression methods, it should be noted that the XVC algorithm remains undervalued, although its results are higher. Algorithm AV1 can be considered the next in order of importance. It has been established that the latest compression algorithms show higher performance if compared to their predecessors. It has also been shown that with a small distance between the optical centers of the captured images, the use of video compression algorithms is preferable to the use of image compression algorithms, since they show better results in both three-dimensional and four-dimensional versions.Conclusions. A comparison of the results obtained shows the need to use algorithms from the video compression family (XVC, AV1) on installations with a long baseline (mounted on camera stands). When working with integrated light field cameras (Lytro) and setting the capture with a short baseline, it is recommended to use image compression algorithms (JPEG). In general, video compression algorithms are recommended, in particular XVC, since on average it shows an acceptable level of PSNR in both the case of a short and long installation baseline.

Discrete Cosine Transform (DCT) has an important role in image compression. This paper presents a fast 2D-DCT architecture for hardware efficient embedded systems and power limited applications such as Internet of Things (IoT). The proposed design both from a structure point of view and operation reduction point of view has two headed approaches toward the problem of image and video compression. It includes a modified high speed architecture using an extra operational reducing technique. Reduction of operations occurs in two stages while computing 8-point DCT transform of the blocks. Defining the appropriate threshold for comparing DCT domain of two rows of an 8*8 block. Approximating DCT operations column-wise is the additional approach of the paper. The architecture is implemented on Xilinx Vivado 2018.2 with VHDL language on Artix-7 FPGA. 207 MHz clock frequency has achieved.

Motion compensation is one of the most essential methods for any video compression algorithm. Video frame prediction is a task analogous to motion compensation. In recent years, the task of frame prediction is undertaken by deep neural networks (DNNs). In this thesis we create a DNN to perform learned frame prediction and additionally implement a codec that contains our DNN. We train our network using two methods for two different goals. Firstly we train our network based on mean square error (MSE) only, aiming to obtain highest PSNR values at frame prediction and video compression. Secondly we use adversarial training to produce visually more realistic frame predictions. For frame prediction, we compare our method with the baseline methods of frame difference and 16x16 block motion compensation. For video compression we further include x264 video codec in the comparison. We show that in frame prediction, adversarial training produces frames that look sharper and more realistic, compared MSE based training, but in video compression it consistently performs worse. This proves that even though adversarial training is useful for generating video frames that are more pleasing to the human eye, they should not be employed for video compression. Moreover, our network trained with MSE produces accurate frame predictions, and in quantitative results, for both tasks, it produces comparable results in all videos and outperforms other methods on average. More specifically, learned frame prediction outperforms other methods in terms of rate-distortion performance in case of high motion video, while the rate-distortion performance of our method is competitive with that of x264 in low motion video.

Images with JPEG format using discrete cosine transform (DCT) is selected for most popular compression standards. Enhancement in the compressed domain offers two major advantages, including low computational complexity and storage space. However, the compression is achieved by block-based transform; therefore, it is hard to enhance image globally. In this paper, the main issue is to develop a global enhancement method that effectively reduces the introduced blocking artifacts and achieves excellent visual quality of enhancement. We propose a combined DCT matrix representation, which consists of 8n?×?8n pixel arrays, to enhance the global information on images for removing block artifacts in compressed-domain. In addition, we propose the multi-enhancement factors based on spatial frequency for image enhancement in compressed domain. From the simulation results, the proposed method achieves not only excellent improvement for image enhancement but also reducing the blocking artifacts significantly.