Automatic network-adaptive ultra-low-bit-rate video coding

Chien, Wei-Jung; Lam, Tuyet-Trang; Abousleman, Glen P.; Karam, Lina J.

doi:10.1117/12.665069

Cited by 3 publications

(8 citation statements)

References 2 publications

(2 reference statements)

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“…To achieve improved rate-distortion performance as compared to the video coder in [2], the proposed system incorporates motion compensation into the predictive coding loop. In most applications, sophisticated motion estimation algorithms are employed to maximize the rate-distortion performance of the video coder.…”

Section: Motion-compensated Video Codingmentioning

confidence: 99%

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Super-resolution-based enhancement for real-time ultra-low-bit-rate video coding

2007

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This paper presents a software-only, real-time video coder/decoder (codec) with super-resolution-based enhancement for ultra-low-bit-rate compression. The codec incorporates a modified JPEG2000 core and interframe predictive coding, and can operate with network bandwidths as low as 500 bits/second. Highly compressed video exhibits severe coding artifacts that degrade visual quality. To lower the level of noise and retain the sharpness of the video frames, we build on our previous work in super-resolution-based video enhancement and propose a new version that is suitable for real-time video coding systems. The adopted super-resolution-based enhancement uses a constrained set of motion vectors that is computed from the original (uncompressed) video at the encoder. Artificial motion is also added to the difference frame to maximize the enhancement performance. The encoder can transmit either the full set of motion vectors or the constrained set of motion vectors depending upon the available bandwidth. At the decoder, each pixel of the decoded frame is assigned to a motion vector from the constrained motion vector set. L 2 -norm minimization super-resolution is then applied to the decoded frame set (previous frame, current frame, and next frame). A selective motion estimation scheme is proposed to prevent ghosting, which otherwise would result from the superresolution enhancement when the motion estimation fails to find appropriate motion vectors. Results using the proposed system demonstrate significant improvements in the quantitative and visual quality of the coded video sequences.

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Section: Motion-compensated Video Codingmentioning

confidence: 99%

“…In our previous work [2], we proposed an ultra-low-bit-rate video coding system that is based on a modified JPEG2000 image compression core. The system can accommodate various bandwidth requirements with many selectable compression preferences such as transmission rate, frame rate, GOP size, etc.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution-based enhancement for real-time ultra-low-bit-rate video coding

2007

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“…The output symbols from each system framework [2]. As described previously, the pass are entropy coded using a context-adaptive video codec is based on a modified JPEG2000 codec arithmetic encoder, and a set of truncation points of rate with predictive coding as described in [2]. and distortion (R-D) pairs are calculated for each codeblock.…”

Section: Ultra-low-bit-rate Video Codingmentioning

confidence: 99%

“…The video encoder design is based upon a modified JPEG2000 image compression core [1] and a motion compensated predictive coding wrapper with built-in zero-motion, full-search, or hexagonal search algorithms [2]. The procedural flow of the server can be described as follows.…”

Section: Ultra-low-bit-rate Video Codingmentioning

confidence: 99%

Target-tracking-based ultra-low-bit-rate video coding

Abousleman

2009

MILCOM 2009 - 2009 IEEE Military Communications Conference

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In this paper, we present an automated region-ofinterest-based video coding system for use in ultra-lowbandwidth applications.Region-of-interest (ROJ) coding methodology specifies that targets or ROIs be coded at higher fidelity using a greater number of available bits, while the remainder of the scene or background is coded using fewer bits. This allows the target regions within the scene to be well preserved, while dramatically reducing the number of bits required to code the video sequence, thus reducing the transmission bandwidth and storage requirements. In the proposed system, the ROI contours are specified automatically by a video target detection and tracking algorithm that continuously monitors the incoming video stream for the presence oftargets. When targets appear in the scene, the detection/tracking stage feeds the target position and size information to the video compression stage, which applies a greater percentage of available bits to these areas, thereby preserving their appearance relative to the non-target or background portion of the scene. In the proposed system, position information is updated in real time and is efficiently exchanged between the transmitter and receiver. Coding examples are presented for infrared (IR) video sequences to demonstrate the outstanding performance of the proposed system.

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“…In our previous work [2], we proposed an ultra-low-bit-rate video coding system that is based on a modified JPEG2000 image compression core. Although this system can accommodate various bandwidth requirements with many selectable compression preferences, such as transmission rate, frame rate, group-of-picture (GOP) size, etc., the video quality provided may not be sufficient to monitor various regions of interest within the frame.…”

Section: Introductionmentioning

confidence: 99%

Region-of-interest-based ultra-low-bit-rate video coding

et al. 2008

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In this paper, we present a region-of-interest-based video coding system for use in real-time applications. Region-ofinterest (ROI) coding methodology specifies that targets or ROIs be coded at higher fidelity using a greater number of available bits, while the remainder of the scene or background is coded using a fewer number of bits. This allows the target regions within the scene to be well preserved, while dramatically reducing the number of bits required to code the video sequence, thus reducing the transmission bandwidth and storage requirements. In the proposed system, the ROI contours can be selected arbitrarily by the user via a graphical user interface (GUI), or they can be specified via a text file interface by an automated process such as a detection/tracking algorithm. Additionally, these contours can be specified at either the transmitter or receiver. Contour information is efficiently exchanged between the transmitter and receiver and can be adjusted on the fly and in real time. Coding results are presented for both electro-optical (EO) and infrared (IR) video sequences to demonstrate the performance of the proposed system.

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Automatic network-adaptive ultra-low-bit-rate video coding

Cited by 3 publications

References 2 publications

Super-resolution-based enhancement for real-time ultra-low-bit-rate video coding

Super-resolution-based enhancement for real-time ultra-low-bit-rate video coding

Target-tracking-based ultra-low-bit-rate video coding

Region-of-interest-based ultra-low-bit-rate video coding

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